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Due to the capacity to process numerical data and generate highly interpretable results, neighborhood rough set theory has been widely used for data classification. However, there are two drawbacks that limit its performance. One is that the existing neighborhood classifiers suffer from insufficient information extraction due to the lack of a training process with feature selection. The other is that the neighborhood radius requires artificial parameters to adapt to different data distributions. To overcome the above shortcomings, a neighborhood classifier based on adaptive radius selection and attribute reduction (NRS-ASAR) is proposed. First, the K-nearest neighbor algorithm and the majority voting principle are introduced to eliminate noisy samples. Next, the training correlation radius is defined based on attribute reduction in which a relationship between the neighborhood radius and the features is established. Then, to overcome the subjectivity of the parameter in neighborhood classifier, an adaptive neighborhood radius called nearest neighborhood radius is defined. Finally, experimental results demonstrate that NRS-ASAR has better classification performance. [ABSTRACT FROM AUTHOR]

To address the challenges of high computational complexity and the tendency to get stuck in local optima during at- tribute reduction within traditional rough set theory, this paper proposed an innovative rough set attribute reduction algorithm based on differential teaching-learning optimization (AR-DTLBO). Leveraging the global search capabilities of the differential teaching-learning optimization algorithm along with the strengths of rough set theory in handling imprecise and uncertain data, the algorithm aimed to optimize the process. Firstly, it enhanced the teaching-learning optimization algorithm by introducing an adaptive teaching factor and a differential mutation strategy, thereby enhancing its search capabilities and optimization performance. Subsequently, it refined the attribute reduction process through the improved teaching-learning optimization algorithm s teaching and learning phases, effectively reducing the dimensionality and complexity of datasets. Finally, it conducted comparative experiments between the proposed AR-DTLBO algorithm and six other algorithms, using eight datasets from the UCI database. The experimental results demonstrate that the proposed algorithm achieves favorable outcomes in terms of reduction length, reduction time, reduction rate, and classification accuracy. This successful reduction and optimization of datasets not only reduces redundant information but also enhances the precision of decision rules. These findings provide valuable sup- port for decision analysis, data mining, and other related fields. [ABSTRACT FROM AUTHOR]

With the continuous expansion of databases, the extraction of information has been an urgent research topic in many fields. As an effective method to remove redundant attributes, attribute reduction demonstrates extraordinary ability in simplifying information systems. This paper applies a novel form of conditional entropy to investigate the attribute reduction in incomplete information systems. Firstly, a novel definition of conditional entropy is introduced based on tolerance relation. Additionally, in order to reduce time complexity, we propose a binsearch heuristic attribute-reduction algorithm with conditional entropy as heuristic knowledge. Furthermore, two examples are used to illustrate the feasibility and validity of the reduction algorithm. [ABSTRACT FROM AUTHOR]

Attribute reduction through the combined approach of Rough Sets (RS) and algebraic topology is an open research topic with significant potential for applications. Several research works have introduced a strong relationship between RS and topology spaces for the attribute reduction problem. However, the mentioned recent methods followed a strategy to construct a new measure for attribute selection. Meanwhile, the strategy for searching for the reduct is still to select each attribute and gradually add it to the reduct. Consequently, those methods tended to be inefficient for high-dimensional datasets. To overcome these challenges, we use the separability property of Hausdorff topology to quickly identify distinguishable attributes, this approach significantly reduces the time for the attribute filtering stage of the algorithm. In addition, we propose the concept of Hausdorff topological homomorphism to construct candidate reducts, this method significantly reduces the number of candidate reducts for the wrapper stage of the algorithm. These are the two main stages that have the most effect on reducing computing time for the attribute reduction of the proposed algorithm, which we call the Cluster Filter Wrapper algorithm based on Hausdorff Topology. Experimental validation on the UCI Machine Learning Repository Data shows that the proposed method achieves efficiency in both the execution time and the size of the reduct. [ABSTRACT FROM AUTHOR]

Most of the current attribute reduction methods use the measure to define the reduct, such as the positive region of rough set theory (RS), information entropy, and distance. However, the size of the reduct based on the measures is still limited. To cope with this problem, we propose a new approach of attribute reduction based on using the intuitionistic fuzzy topology (IFT). Firstly, a new IFT structure based on the pre-order relation and the intuitionistic fuzzy base (IF-base) structure is introduced. Secondly, a new measure is proposed to evaluate the significance of the attribute based on the IF subbase. Finally, the new reduction algorithms based on the IF-base filter and filter-wrapper methods are presented. The theoretical and experimental results show that the proposed method is efficient in terms of size and accuracy of the reduct. Specifically, the reduct of the F_IFT algorithm has an average size of 50% smaller, and the FW_IFT algorithm has an average accuracy of 10% greater than those of the related algorithms. Significantly, the algorithm FW_IFT can very remove noisy attributes. The classification accuracy of the reduct is 15% higher than that of the original set of attributes. [ABSTRACT FROM AUTHOR]

A fuzzy relation is a basic concept of fuzzy set theory. Fuzzy entropy as information entropy for a fuzzy relation is to measure the uncertainty of a fuzzy relation. Some measures on the uncertainty of a fuzzy relation have been presented in recent years by generalizing information entropy. However, a fuzzy relation can induce to a family of upper-fuzzy sets and a family of lower-fuzzy sets, respectively; the existing methods consider only the upper-fuzzy sets in a fuzzy relation and do not address the lower-fuzzy sets. Moreover, these methods sometimes need to use the equality between fuzzy sets, but the equality between fuzzy sets is actually very difficult to achieve. To solve the above problem, this paper proposes new fuzzy entropy based on class-consistent technology and considers its application in attribute reduction for heterogeneous data. Class-consistent technology is a technique for dealing with approximate equality of values. It replaces equality with approximate equality between two values in unit close interval. First of all, based on this technology, two equivalence relations are put forward by means of upper-fuzzy and the lower-fuzzy sets in a fuzzy relation. Then, upper-fuzzy entropy and lower-fuzzy entropy are presented by using these two equivalence relations. Next, new fuzzy entropy is constructed to measure the uncertainty of a fuzzy relation, and the constructed fuzzy entropy overcomes the weakness of the existing methods. Moreover, new fuzzy conditional entropy is defined. Finally, new fuzzy conditional entropy is applied to carry out attribute reduction for heterogeneous data. The experiment results confirm that the given reduction method is more effective than other methods. [ABSTRACT FROM AUTHOR]

Abstract With the increasingly severe energy supply and environmental pressures, high-end equipment is gradually adopted to reduce the carbon emissions of manufacturing industry which makes its low-carbon structural design a critical research hotspot. The best structural scheme can be got by multi-attribute decision-making (MADM) with design requirements. However, the decision-making attributes in the structural design of high-end equipment are too many at first and low-carbon attributes are seldom fully considered. Moreover, there are a large amount of related data with linguistic vagueness, interval uncertainty, and information incompleteness, which fail to be handled simultaneously. There, this paper proposes an integrated MADM method of low-carbon structural design for high-end equipment based on attribute reduction considering incomplete interval uncertainties. First, distribution reduction of low-carbon structural design is carried out to obtain the minimum attribute set and encompass low-carbon attributes comprehensively. Second, a collaborative filtering algorithm is utilized to complete the missing data in the subsequent design process. Third, interval rough numbers (IRNs) are integrated into DEMATEL-ANP (DANP) and multi-attribute border approximation area comparison (MABAC) to quickly rank the alternative schemes for high-end equipment and determine which is the best. The rationality and robustness of the proposed method are verified through the case study and comparative analysis of a hydraulic forming machine.

Abstract This paper addresses the current existence of attribute reduction algorithms for incomplete hybrid decision-making systems, including low attribute reduction efficiency, low classification accuracy and lack of consideration of unlabeled data types. To address these issues, this paper first redefines the weakly labeled relative neighborhood discernibility degree and develops a non-dynamic attribute reduction algorithm. In addition, this paper proposes an incremental update mechanism for weakly tagged relative neighborhood discernibility degree and introduces a new dynamic attribute reduction algorithm for increasing the set of objects based on it. Meanwhile, this paper also compares and analyses the improved algorithm proposed in this study with two existing attribute reduction algorithms using 8 data sets in the UCI database. The results show that the dynamic attribute reduction algorithm proposed in this paper achieves higher attribute reduction efficiency and classification accuracy, which further validates the effectiveness of the algorithm proposed in this paper.

Dynamic reduction algorithms have become an important part of attribute reduction research because of their ability to perform dynamic updates without the need to retrain the original model. To enhance the efficiency of variable precision reduction algorithms in processing dynamic data, research has been conducted from the perspective of the construction process of the discernibility matrix. By modifying the decision values of some samples through an absolute majority voting strategy, a connection between variable precision reduction and positive region reduction has been established. Considering the increase and decrease of samples, dynamic variable precision reduction algorithms have been proposed. For four cases of sample increase, four corresponding scenarios have been discussed, and judgment conditions for the construction of the discernibility matrix have been proposed, which has led to the development of a dynamic variable precision reduction algorithm for sample increasing (DVPRA-SI). Simultaneously, for the scenario of sample deletion, three corresponding scenarios have been proposed, and the judgment conditions for the construction of the discernibility matrix have been discussed, which has resulted in the development of a dynamic variable precision reduction algorithm for sample deletion (DVPRA-SD). Finally, the proposed two algorithms and existing dynamic variable precision reduction algorithms were compared in terms of the running time and classification precision, and the experiments demonstrated that both algorithms are feasible and effective. [ABSTRACT FROM AUTHOR]

Hesitant fuzzy information systems have been widely applied in decision-making due to their ability to handle uncertain information. In addition, dominance relationships are often taken into account in many practical decision-making problems. Therefore, it is of great significance to conduct research on hesitant fuzzy information systems involved with dominance relations. In this study, we introduce dominance relations in a hesitant fuzzy information system to make it a dominance-based hesitant fuzzy information system, which can provide a solid new idea for comparing hesitant fuzzy elements. Furthermore, a hesitant fuzzy dominance-based rough set model is constructed, and an attribute reduction method is designed to simplify the dominance-based hesitant fuzzy information system. Further, we propose lower and upper approximation discernibility matrices in the dominance-based hesitant fuzzy decision information system to extract decision rules. In addition, two numerical examples are given to demonstrate the effectiveness of the proposed attribute reduction methods. [ABSTRACT FROM AUTHOR]

For decision makers in various fields, the best way to deal with complicated decision-making problems quickly and efficiently is to simplify the complicated problems as much as possible. Inspired by this kind of social management demand, this paper proposes a novel attribute reduction method using evidence theory in hesitant fuzzy data sets. The method ensures belief and plausibility sum and considers internal and external significance measures. It extends classical evidence theory to hesitant fuzzy data sets and introduces internal and external belief significance measures and internal and external plausibility significance measures based on belief and plausibility functions. The properties of belief and plausibility reduction in hesitant fuzzy data sets are studied, along with the relationship between internal and external significance measures. The attribute reduction method based on significance measure has significant effects on complex information systems and can improve the execution efficiency of decision-makers. And the impressive application value of these theories is demonstrated. [ABSTRACT FROM AUTHOR]

Attribute reduction is a typical topic in the field of rough sets. As an extension of this topic, test cost sensitive attribute reduction has garnered considerable attention in recent years, and scholars have made many achievements. However, existing research commonly operates under the assumption that test costs are exact values, disregarding the challenges associated with quantifying test costs accurately in certain real-world contexts. In light of the situation, this paper employs the form of interval values to represent the possible range of test costs and then studies the problem of attribute reduction based on interval-valued test costs. Firstly, a theoretical model for interval-valued test cost sensitive attribute reduction is constructed by utilizing a ranking method of intervals. In this model, some important concepts and properties are discussed. Especially, considering that the risk attitudes of different decision-makers may affect the decision results, an optimization problem related to risk attitude is formulated. Secondly, a backtracking algorithm and a heuristic algorithm are developed for tackling the optimization problem, along with the application of a competition strategy to enhance the heuristic algorithm's performance. Finally, the performance of the two algorithms is evaluated on multiple UCI datasets, and comparisons are drawn with several state-of-the-art attribute reduction methods. Experimental analyses well illustrate the effectiveness and superiority of the suggested algorithms. It is hoped that this work provides new insights into cost sensitive attribute reduction from the perspective of cost uncertainty and provides a reference for decision-making problems. [ABSTRACT FROM AUTHOR]

With the increasingly severe energy supply and environmental pressures, high-end equipment is gradually adopted to reduce the carbon emissions of manufacturing industry which makes its low-carbon structural design a critical research hotspot. The best structural scheme can be got by multi-attribute decision-making (MADM) with design requirements. However, the decision-making attributes in the structural design of high-end equipment are too many at first and low-carbon attributes are seldom fully considered. Moreover, there are a large amount of related data with linguistic vagueness, interval uncertainty, and information incompleteness, which fail to be handled simultaneously. There, this paper proposes an integrated MADM method of low-carbon structural design for high-end equipment based on attribute reduction considering incomplete interval uncertainties. First, distribution reduction of low-carbon structural design is carried out to obtain the minimum attribute set and encompass low-carbon attributes comprehensively. Second, a collaborative filtering algorithm is utilized to complete the missing data in the subsequent design process. Third, interval rough numbers (IRNs) are integrated into DEMATEL-ANP (DANP) and multi-attribute border approximation area comparison (MABAC) to quickly rank the alternative schemes for high-end equipment and determine which is the best. The rationality and robustness of the proposed method are verified through the case study and comparative analysis of a hydraulic forming machine. [ABSTRACT FROM AUTHOR]

This paper addresses the current existence of attribute reduction algorithms for incomplete hybrid decision-making systems, including low attribute reduction efficiency, low classification accuracy and lack of consideration of unlabeled data types. To address these issues, this paper first redefines the weakly labeled relative neighborhood discernibility degree and develops a non-dynamic attribute reduction algorithm. In addition, this paper proposes an incremental update mechanism for weakly tagged relative neighborhood discernibility degree and introduces a new dynamic attribute reduction algorithm for increasing the set of objects based on it. Meanwhile, this paper also compares and analyses the improved algorithm proposed in this study with two existing attribute reduction algorithms using 8 data sets in the UCI database. The results show that the dynamic attribute reduction algorithm proposed in this paper achieves higher attribute reduction efficiency and classification accuracy, which further validates the effectiveness of the algorithm proposed in this paper. [ABSTRACT FROM AUTHOR]

Worldwide, the healthcare system is greatly impacted by the changing requirements of the people. Diabetes is a long-lasting condition that can lead to serious complications if not controlled correctly. It is divided into Type 1 (TID) and Type 2 (T2D) diabetes. Research shows that almost 90% of Diabetes cases are T2D, with TID making up around 10% of all Diabetes cases. This paper suggests a Rough-Neuro classification model for identifying Type 2 Diabetes, which includes a two-stage process. The approach includes utilising Rough sets JohnsonReducer to eliminate unnecessary features or characteristics and multi-layer perceptron for illness categorization. The suggested technique seeks to reduce the amount of input characteristics, which results in a reduction in the time needed to train the neural network and the storage space required. The findings show that decreasing the amount of input characteristics results in a lower neural network training time, enhances model performance, and reduces storage needs by 63%. It is worth mentioning that a smaller neural network with only seven hidden layers, trained for 1000 epochs with a learning rate of 0.01, attained the best performance, but time and storage were much decreased. [ABSTRACT FROM AUTHOR]

Type-2 fuzzy information systems (TFISs-2), one interesting form of complex data, are extensions of fuzzy information systems. Uncertainty measurement and attribute reduction of TFISs-2 are key issues when describing and analyzing complex fuzzy data. However, existing researches mainly focus on the traditional fuzzy data, and seldom consider type-2 fuzzy data. This paper focuses on finding an effective indicator to measure the uncertainty of TFISs-2, and carries out the research of attribute reduction based on this indicator. Firstly, the weighted membership distance and the weighted membership similarity are defined, which are used to describe the relation between two objects in TIFSs-2, and σ similarity relations are introduced. Secondly, a fuzzy neighborhood structure is defined based on σ similarity relations. The classical rough set model is extended on TFISs-2, and the definitions of upper and lower approximation, accuracy and roughness are given. Then, a novel indicator called σ self-information is used to describe the uncertainty of TFISs-2, and a heuristic greedy selection attribute reduction algorithm is proposed. Finally, the experimental results show that the constructed uncertainty measurement indicator is suitable for TFISs-2, and the proposed attribute reduction method is effective. This study is beneficial to promote the understanding of complex fuzzy data, and contributes to the data analysis and processing of TFISs-2. [ABSTRACT FROM AUTHOR]

One of the current research gaps in multi-scale data analysis is studying information systems characterized by attributes with interval sets as attribute values and multiple scales. To address this gap, we first introduce the concepts of a multi-scale interval-set information system (MISIS) and a multi-scale interval-set decision table (MISDT). We then define the similarity relation between objects in an MISIS and the corresponding rough approximations. We further propose the positive region optimal scale, the modified conditional entropy optimal scale, and the positive complementary conditional entropy optimal scale in an MISDT. We examine the relationships among these optimal scales in consistent and inconsistent MISDTs and show that the positive region optimal scale and the modified conditional entropy optimal scale are equivalent in a consistent MISDT, while in an inconsistent MISDT, the positive region optimal scale is the same as the positive complementary conditional entropy optimal scale, and the modified conditional entropy optimal scale is not greater than the positive complementary conditional entropy optimal scale. Based on the optimal scale, we also develop attribute reduction approaches in MISDTs. Finally, through experimental analysis of data on the UCI dataset, we verify the effectiveness and reasonableness of our proposed methods. [ABSTRACT FROM AUTHOR]

Factor selection is crucial for any enterprise to make a quick and accurate quotation decision. For the objects of quotation with missing values, traditional rough sets construct their relations (e.g. tolerance, cover) either with objects having known values, or only with those that also include the missing values of the attribute domains. Such classifications may not work well for reduction in many real-world problems. In this paper, by measuring the similarity of objects, an incomplete covering rough set is proposed to derive a cover for attribute reduction. Firstly, a similarity relation is defined by an approximation degree that tunes the relation in line with the semantics of objects, and then a cover is induced. Secondly, a reduct is derived by the relations of objects with respect to covers; the properties of reduction are proven. Finally, an approach is developed by discernibility matrix. The experimental results of the UCI (University of California Irvine) Repository and real-life quotation data sets show the incomplete covering rough set outperforms the compared rough set in the accuracy of factor selection within the comparable computation time. It is also demonstrated that the proposed quotation model is effective in quote prediction with various proportions of missing data. [ABSTRACT FROM AUTHOR]

Neighborhood rough sets, as an effective tool for processing numerical data, is widely used in many fields, such as data mining, machine learning and decision-making system. However, most of the existing neighborhood rough set-based attribute reduction algorithms have low efficiency. To address the limitation, this paper has proposed an efficient positive region search algorithm based on multiple hash buckets and multiple granularity mechanisms. This algorithm achieves a more accurate neighborhood extent by superimposing the effects of multiple hash buckets, and accelerates positive region searching through the idea of multiple granularity. In addition, on the foundation the positive region search algorithm, we improved the existing algorithm and proposed an attribute reduction algorithm based on multi-hash bucket and multi-granularity. To further remove the redundant attributes, the two algorithms mentioned above are applied into a novel attribute reduction approach based on random walk. Experiments conducted on UCI datasets show that our attribute reduction algorithm has high efficiency. Moreover, attribute reduction approach we proposed can further compress the reduced attribute set, and the results maintain similar or even better classification accuracy. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Attribute reduction is a core technique in the rough set domain and an important step in data preprocessing. Researchers have proposed numerous innovative methods to enhance the capability of attribute reduction, such as the emergence of multi-granularity rough set models, which can effectively process distributed and multi-granularity data. However, these innovative methods still have numerous shortcomings, such as addressing complex constraints and conducting multi-angle effectiveness evaluations. Based on the multi-granularity model, this study proposes a new method of attribute reduction, namely using multi-granularity neighborhood information gain ratio as the measurement criterion. This method combines both supervised and unsupervised perspectives, and by integrating multi-granularity technology with neighborhood rough set theory, constructs a model that can adapt to multi-level data features. This novel method stands out by addressing complex constraints and facilitating multi-perspective effectiveness evaluations. It has several advantages: (1) it combines supervised and unsupervised learning methods, allowing for nuanced data interpretation and enhanced attribute selection; (2) by incorporating multi-granularity structures, the algorithm can analyze data at various levels of granularity. This allows for a more detailed understanding of data characteristics at each level, which can be crucial for complex datasets; and (3) by using neighborhood relations instead of indiscernibility relations, the method effectively handles uncertain and fuzzy data, making it suitable for real-world datasets that often contain imprecise or incomplete information. It not only selects the optimal granularity level or attribute set based on specific requirements, but also demonstrates its versatility and robustness through extensive experiments on 15 UCI datasets. Comparative analyses against six established attribute reduction algorithms confirms the superior reliability and consistency of our proposed method. This research not only enhances the understanding of attribute reduction mechanisms, but also sets a new benchmark for future explorations in the field. [ABSTRACT FROM AUTHOR]

The presence of numerous uncertainties in hybrid decision information systems (HDISs) renders attribute reduction a formidable task. Currently available attribute reduction algorithms, including those based on Pawlak attribute importance, Skowron discernibility matrix, and information entropy, struggle to effectively manages multiple uncertainties simultaneously in HDISs like the precise measurement of disparities between nominal attribute values, and attributes with fuzzy boundaries and abnormal values. In order to address the aforementioned issues, this paper delves into the study of attribute reduction withinHDISs. First of all, a novel metric based on the decision attribute is introduced to solve the problem of accurately measuring the differences between nominal attribute values. The newly introduced distance metric has been christened the supervised distance that can effectively quantify the differences between the nominal attribute values. Then, based on the newly developed metric, a novel fuzzy relationship is defined from the perspective of "feedback on parity of attribute values to attribute sets". This new fuzzy relationship serves as a valuable tool in addressing the challenges posed by abnormal attribute values. Furthermore, leveraging the newly introduced fuzzy relationship, the fuzzy conditional information entropy is defined as a solution to the challenges posed by fuzzy attributes. It effectively quantifies the uncertainty associated with fuzzy attribute values, thereby providing a robust framework for handling fuzzy information in hybrid information systems. Finally, an algorithm for attribute reduction utilizing the fuzzy conditional information entropy is presented. The experimental results on 12 datasets show that the average reduction rate of our algorithm reaches 84.04%, and the classification accuracy is improved by 3.91% compared to the original dataset, and by an average of 11.25% compared to the other 9 state-of-the-art reduction algorithms. The comprehensive analysis of these research results clearly indicates that our algorithm is highly effective in managing the intricate uncertainties inherent in hybrid data. [ABSTRACT FROM AUTHOR]

Attribute reduction is an important method in data analysis and machine learning, and it usually relies on algebraic and informational measures. However, few existing informational measures have considered the relative information of decision class cardinality, and the fusion application of algebraic and informational measures is also limited, especially in attribute reductions for interval-valued data. In interval-valued decision systems, this paper presents a coverage-credibility-based condition entropy and an improved rough decision entropy, further establishes corresponding attribute reduction algorithms for optimization and applicability. Firstly, the concepts of interval credibility, coverage and coverage-credibility are proposed, and thus, an improved condition entropy is defined by virtue of the integrated coverage-credibility. Secondly, the fused rough decision entropy is constructed by the fusion of improved condition entropy and roughness degree. By introducing the coverage-credibility, the proposed uncertainty measurements enhance the relative information of decision classes. In addition, the nonmonotonicity of the improved condition entropy and rough decision entropy is validated by theoretical proofs and experimental counterexamples, with respect to attribute subsets and thresholds. Then, the two rough decision entropies drive monotonic and nonmonotonic attribute reductions, and the corresponding reduction algorithms are designed for heuristic searches. Finally, data experiments not only verify the effectiveness and improvements of the proposed uncertainty measurements, but also illustrate the reduction algorithms optimization through better classification accuracy than four comparative algorithms. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the problem that attributes in the existing neighborhood rough set model all have the same weight, which cannot ensure that the key attributes can be retained in attribute approximation, this paper proposed an attribute approximation algorithm based on information entropy weighting. Firstly, it adopted interclass entropy and intraclass entropy strategies to assign weights to attributes based on the principle of maximising interclass entropy and minimising intraclass entropy. Secondly, it constructed a weighted neighborhood rough set model based on weighted neighborhood relationships. Finally, it assessed the importance of attribute subsets based on dependency relationships to achieve attribute simplification. Comparison experiments with other three attribute approximation algorithms on UCI-based dataset show that the proposed algorithm can effectively remove redundancy and improve classification accuracy. [ABSTRACT FROM AUTHOR]

In the era of big data, discovering knowledge from incomplete and mixed complex systems is an important research topic that has always been the focus and favor of researchers. Attribute reduction is one of the important research contents in the fields of knowledge discovery, data mining and etc. For improving the performance of attribute reduction, some scholars have proposed many approaches and acceleration mechanisms. As an effective data processing model, the matrix has some bottlenecks in obtaining the reduct efficiently, and there is less research on its accelerated reduction strategies and algorithms. Therefore, the main motivation of this article is to explore and apply various acceleration strategies in matrix reduction methods to improve the efficiency of knowledge discovery in incomplete hybrid decisions systems (IHDSs). First, for incomplete mixed data, the combination tolerance relation is presented. Based on the combination tolerance relation, the corresponding operations and properties of the related matrix are studied, and a matrix-based positive region reduction method is proposed. Next, we develop the incremental computing strategy for updating the matrices to avoid re-calculation and the gradually reducing processing scale strategy for compressing the processing volume in the reduction process; then, two types of matrix-based heuristic reduction acceleration algorithms are designed to obtain a reduct of IHDSs. Finally, some experiments are carried out on nine typical data sets in 15 UCI data sets to verify the effectiveness and efficiency of proposed algorithms. [ABSTRACT FROM AUTHOR]

As a very prominent research application of the theory of rough sets, attribute reduction technique has made significant strides in a lot of fields, including decision making, granular computing, etc. In particular, fuzzy attribute reduction approaches contribute greatly in the presence of uncertain data. However, most of fuzzy relations used in these approaches lack the discriminant ability to sample similarity, failing to identify the feature significance satisfactorily. In this article, a novel scheme using the shared neighborhood fuzzy uncertainties is proposed. Firstly, the concept of shared neighborhood is formulated, and then employed to establish the fuzzy similarity relation that effectively captures the sample similarity. Secondly, two fuzzy uncertainty measures named joint entropy and discrimination index based on shared neighborhood fuzzy relation are defined, which can quantify the feature’s significance to the uncertainty characterization. Finally, two heuristic searching algorithms are designed to identify reducts aimed at minimizing the fuzzy uncertainties. Some comparative studies are investigated to examine the advantage of the designed reduction algorithms in classifier modeling. The reported analyses on public data sets verify that the designed algorithms outperform some representative and latest algorithms.

With the continuous expansion of databases, the extraction of information has been an urgent research topic in many fields. As an effective method to remove redundant attributes, attribute reduction demonstrates extraordinary ability in simplifying information systems. This paper applies a novel form of conditional entropy to investigate the attribute reduction in incomplete information systems. Firstly, a novel definition of conditional entropy is introduced based on tolerance relation. Additionally, in order to reduce time complexity, we propose a binsearch heuristic attribute-reduction algorithm with conditional entropy as heuristic knowledge. Furthermore, two examples are used to illustrate the feasibility and validity of the reduction algorithm.

Fuzzy rough sets (FRSs) play a significant role in the field of data analysis, and one of the common methods for constructing FRSs is the use of the fuzzy logic operators. To further extend FRSs theory to more diverse information backgrounds, this article proposes a covering variable precision fuzzy rough set model based on overlap functions and fuzzy β-neighbourhood operators (OCVPFRS). Some necessary properties of OCVPFRS have also been studied in this work. Furthermore, multi-label classification is a prevalent task in the realm of machine learning. Each object (sample or instance) in multi-label data is associated with various labels (classes), and there are numerous features or attributes that need to be taken into account within the attribute space. To enhance various performance metrics in the multi-label classification task, attribute reduction is an essential pre-processing step. Therefore, according to overlap functions and fuzzy rough sets’ excellent work on applications: such as image processing and multi-criteria decision-making, we establish an attribute reduction method suitable for multi-label data based on OCVPFRS. Through a series of experiments and comparative analysis with existing multi-label attribute reduction methods, the effectiveness and superiority of the proposed method have been verified. [ABSTRACT FROM AUTHOR]

In real-world scenarios, datasets generally exhibit containing mixed-type of attributes and imbalanced classes distribution, and the minority classes in the data are the primary research focus. Attribute reduction is a key step in the data preprocessing process, but traditional attribute reduction methods commonly overlook the significance of minority class samples, causing the critical information possessed in minority class samples to damage and decrease the performance of classification. In order to address this issue, we develop an attribute reduction algorithm based on a composite entropy-based uncertainty measure to handle imbalanced mixed-type data. To begin with, we design a novel oversampling method based on the three-way decisions boundary region to synthesize the samples of minority class, for the boundary region to contain more high-quality samples. Then, we propose an attribute measure to select candidate attributes, which considers the boundary entropy, degree of dependency and weight of classes. On this basis, a composite entropy-based uncertainty measure guided attribute reduction algorithm is developed to select the attribute subset for the imbalanced mixed-type data. Experimental on UCI imbalanced datasets, as well as the results indicate that the developed attribute reduction algorithm is significantly outperforms compared to other attribute reduction algorithms, especially in total AUC, F1-Score and G-Mean. [ABSTRACT FROM AUTHOR]

In order to address the issue of test cost being rarely considered in rough set attribute reduction, a three-way neighborhood attribute reduction algorithm based on test cost is proposed in this paper. The proposed algorithm calculates the attribute importance according to the frequency and proportion of each attribute in the neighborhood resolution matrix, and combines the test cost of the attributes to construct the the cost performance index to guide the selection of attributes. Three way decision-making method is employed to partition attribute sets, which provides data support for the attribute reduction process. Comparative experiments are conducted on seven UCI public datasets, which demonstrate that the proposed algorithm yields a smaller attribute reduction set compared to the comparison algorithm.Moreover, the proposed algorithm exhibits a shorter running time and lower test cost without compromising classification accuracy. Furthermore, an application example based on fiscal revenue prediction is provided to further validate the effectiveness and practicality of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Tri-level attribute reduction is an interesting topic that aims to reduce the data dimensionality from different levels and granularity perspectives. However, existing research exhibits limitations, mainly in handling symbolic data, lack of effective reduction algorithms, and scarcity of data experiments and performance evaluations, which would be an obstacle to the further development of tri-level attribute reduction in theory and application. Hence, we systematically investigate tri-level attribute reduction based on neighborhood rough sets (NRSs) for numerical data. We first give the class-specific and object-specific attribute reduction conditions based on NRS, respectively. Furthermore, we explore and analyze relationships of tri-level reducts. From the perspective of forward and backward reduction, we propose algorithms of class-specific attribute reduction based on dependency degree, and object-specific reduction algorithms based on inconsistency degree. Finally, we introduce a novel metric to validate the efficiency of specific class and specific object attribute reductions. The results of data experiments show the feasibility and effectiveness of tri-level attribute reduction based on NRS in data analysis. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Railway Science & Engineering is the property of Journal of Railway Science & Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Although neighborhood rough set(NRS) based attribute reduction methods have achieved excellent performance in many scenarios, the efficiency and robustness of these methods have not attracted much attention. In this study, we propose a fast fixed granular-ball model (FFGB) for attribute reduction in label noise environments. In FFGB, we propose a fast neighborhood search mechanism to improve the efficiency of NRS. This fast mechanism reduces the neighborhood search range from the universe to a neighborhood and reduces the time complexity of the neighborhood calculation to much less than O (n 2) . Based on the fast mechanism, we propose FFGB model whose definitions are relaxed to be robust to against label noise. In addition, a FFGB attribute reduction algorithm is designed. Finally, we apply the FFGB attribute reduction to medical diagnosis. The experimental results indicate that FFGB is more efficient and robust than the comparison methods. [ABSTRACT FROM AUTHOR]

Attribute reduction is capable of reducing the dimensionality of data and improving the performance of data mining. As a reasonable representative of relationships between samples, neighbor inconsistent pair focuses on measuring uncertainty in information systems. Nevertheless, classical attribute reduction methods are static and unsuitable for data with variations. Additionally, it is inevitable for data to undergo changes in real-life scenarios, such as an increase in the number of samples. Therefore, it is essential to identify an efficient method for reducing the dimensionality of the dataset while preserving the classification accuracy. Inspired by these deficiencies, our focus lies on developing effective and efficient incremental methods that employ the neighbor inconsistent pair selection strategy for decision tables involving object variations. At first, some concepts related to rough sets, simplified decision tables and neighbor inconsistent pairs are introduced. Then, the heuristic attribute reduction algorithms for dynamic decision tables with the variation of object sets are designed by neighbor inconsistent pairs. Next, a novel feature selection procedure, which we refer to as incremental neighbor inconsistent pair selection, is proposed to update reducts for dynamic decision tables with the variation of object sets. Finally, two incremental attribute reduction algorithms based on neighbor inconsistent pair selection are designed. Furthermore, experiments are conducted on real datasets to validate the effectiveness and benefits of the proposed incremental algorithms. The results indicate that our algorithms exhibit minimal computing time requirements while achieving the highest classification accuracy among at least ten out of thirteen datasets when compared to the comparative algorithms. [ABSTRACT FROM AUTHOR]

As an important extension of the rough set theory, interval-set rough sets provide an effective method to solve the problem that the objective sets cannot be accurately expressed in the rough approximation process and to induce classification rules from incomplete information systems. In practical information systems, the value of an object's decision attribute may be set-valued due to missing decision information or multiple decision values. However, this situation is not considered by any of the existing rough set models. Therefore, to properly deal with the information system where decision attributes are set-valued, we construct the interval-set rough set model on the information system and study attribute reductions of interval-set rough sets. First, we define the information system where decision attributes are set-valued as an extended set-valued decision information system (ESVDIS) and construct the interval-set rough set model on the ESVDIS. Second, the accuracy and the roughness are extended to the interval-set approximation accuracy and the interval-set approximation roughness, respectively. The two extended measures can be used to measure the uncertainty caused by rough approximations. In addition, the dependency degree of interval-set rough sets called the interval-set dependency degree is defined to estimate the significance of attribute subsets. By adopting the interval-set dependency degree, we propose the definition of attribute reduction based on interval-set rough sets and design a heuristic attribute reduction algorithm. Finally, the effectiveness of the proposed attribute reduction algorithm is demonstrated using 12 public data sets. Experimental results show that the model is applicable to some fields, such as missing decision information and group decision-making. [ABSTRACT FROM AUTHOR]

In an era of data-based and information-centric Industry 4.0, extracting potential knowledge and valuable information from data is central to data mining tasks. Yet, the ambiguity, imprecision, incompleteness, and hybrid in real-world data pose tremendous challenges to critical information mining. Accordingly, we propose a new Max-Correlation Min-Redundant (MCMR) attribute reduction model from the uncertainty relation of attributes to avoid information loss in incomplete mixed data. Specifically, the neighbor relations are primarily developed based on the soft computing approach of the neighborhood information system, which divides the objects into neighborhood covers to maximize the utilization of the information in the incomplete mixed data. Then, we detailly analyze the internal and external consistency relationships of the four main uncertainty functions. Based on this, a new MCMR uncertain function is designed with maximum relevance and minimum redundancy. Experiments on nine real-world datasets validate the proposed model can improve data quality by mining critical information in classification tasks and achieving optimal performance with a minimum number of attributes. [ABSTRACT FROM AUTHOR]

In order to address the problem of low algorithm efficiency caused by repeated calculation of the relative importance of attributes in the attribute reduction algorithm based on positive region, this paper proposed a fast attribute reduction algorithm based on knowledge rough entropy from the perspectives of attribute measurement and search strategy. Firstly, it introduced knowledge rough entropy into decision information systems by incorporating knowledge distance to measure the degree of knowledge roughness. Next, it employed knowledge rough entropy as the criterion for evaluating the significance of attributes, assessing the importance of individual attributes. Finally, it ranked all attributes based on attribute importance, and eliminated redundant attributes through dependency, so as to achieve rapid attribute reduction. The proposed algorithm was compared with other three algorithms in terms of running efficiency and classification accuracy on six publicly available datasets. The results demonstrate that the proposed algorithm improves running efficiency by 83.24%,28.77%, and 59.92% respectively compared to other three algorithms. Among the three classifiers, the classification accuracy increases on average by 0.83%,0.63%, and 1.37% respectively. Therefore, the proposed algorithm is able to achieve attribute reduction more quickly while ensuring classification performance. [ABSTRACT FROM AUTHOR]

Formal concept analysis is a widely studied mathematical tool for performing data analysis and processing. Three-way decision is a model of decision making based on human cognition, which decomposes the problem to be solved into three elements and then reprocesses them. Hesitant fuzzy sets use some possible values instead of one, which can reflect the hesitation and uncertainty of the decision makers. This paper combines the three elements together for the first time and proposes a hesitant fuzzy three-way concept lattice model, which not only extends the concept lattice model but also provides a new way to deal with imprecise data. Firstly, the hesitant fuzzy three-way concept lattice model is proposed and the related properties are investigated. Further, two methods of constructing the hesitant fuzzy three-way concept lattice are studied, and the related construction algorithms are given. Thirdly, the attribute reduction based on the hesitant fuzzy three-way concept lattice is proposed, and a heuristic reduction algorithm is given. Finally, the effectiveness of the proposed algorithms for constructing the hesitant fuzzy three-way concept lattice is verified through some experiments, and the efficiency of the algorithm is compared. [ABSTRACT FROM AUTHOR]

The phenomenon of missing data can be seen everywhere in reality. Most typical attribute reduction models are only suitable for complete systems. But for incomplete systems, we cannot obtain the effective reduction rules. Even if there are a few reduction approaches, the classification accuracy of their reduction sets still needs to be improved. In order to overcome these shortcomings, this paper first defines the similarities of intra-cluster objects and inter-cluster objects based on the tolerance principle and the mechanism of knowledge granularity. Secondly, attributes are selected on the principle that the similarity of inter-cluster objects is small and the similarity of intra-cluster objects is large, and then the knowledge granularity attribute model is proposed under the background of clustering; then, the IKAR algorithm program is designed. Finally, a series of comparative experiments about reduction size, running time, and classification accuracy are conducted with twelve UCI datasets to evaluate the performance of IKAR algorithms; then, the stability of the Friedman test and Bonferroni–Dunn tests are conducted. The experimental results indicate that the proposed algorithms are efficient and feasible. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Zhengzhou University (Natural Science Edition) is the property of Journal of Zhengzhou University (Natural Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Zhengzhou University (Natural Science Edition) is the property of Journal of Zhengzhou University (Natural Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Zhengzhou University (Natural Science Edition) is the property of Journal of Zhengzhou University (Natural Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Efficient attribute reduction algorithm capable of handling high dimensional big data is one of the hot topics of rough set theory, and some related researchers have achieved with |C| jobs. In this paper, we present the definition of a marked reduction set and propose a more efficient attribute reduction algorithm (RA-MRS). The RA-MRS includes a batch processing phase and a vibration optimization phase, which reduce the number of jobs from |C| to log2|C|. Additionally, we provide an effective judgment strategy based on MapReduce, which supports the exception processing mechanism of Java to interrupt and advance the current job. Finally, the proposed algorithm is implemented in parallel based on Spark computing framework. The experimental results show that the proposed RA-MRS algorithm is over 99% faster than the classical PAAR_PR algorithm and 70% faster than the algorithm in the literature (Yin et al., 2021).

Hesitant fuzzy information systems have been widely applied in decision-making due to their ability to handle uncertain information. In addition, dominance relationships are often taken into account in many practical decision-making problems. Therefore, it is of great significance to conduct research on hesitant fuzzy information systems involved with dominance relations. In this study, we introduce dominance relations in a hesitant fuzzy information system to make it a dominance-based hesitant fuzzy information system, which can provide a solid new idea for comparing hesitant fuzzy elements. Furthermore, a hesitant fuzzy dominance-based rough set model is constructed, and an attribute reduction method is designed to simplify the dominance-based hesitant fuzzy information system. Further, we propose lower and upper approximation discernibility matrices in the dominance-based hesitant fuzzy decision information system to extract decision rules. In addition, two numerical examples are given to demonstrate the effectiveness of the proposed attribute reduction methods.

Dynamic reduction algorithms have become an important part of attribute reduction research because of their ability to perform dynamic updates without the need to retrain the original model. To enhance the efficiency of variable precision reduction algorithms in processing dynamic data, research has been conducted from the perspective of the construction process of the discernibility matrix. By modifying the decision values of some samples through an absolute majority voting strategy, a connection between variable precision reduction and positive region reduction has been established. Considering the increase and decrease of samples, dynamic variable precision reduction algorithms have been proposed. For four cases of sample increase, four corresponding scenarios have been discussed, and judgment conditions for the construction of the discernibility matrix have been proposed, which has led to the development of a dynamic variable precision reduction algorithm for sample increasing (DVPRA-SI). Simultaneously, for the scenario of sample deletion, three corresponding scenarios have been proposed, and the judgment conditions for the construction of the discernibility matrix have been discussed, which has resulted in the development of a dynamic variable precision reduction algorithm for sample deletion (DVPRA-SD). Finally, the proposed two algorithms and existing dynamic variable precision reduction algorithms were compared in terms of the running time and classification precision, and the experiments demonstrated that both algorithms are feasible and effective.