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In this paper, we study the concept of nearness between L-fuzzy rough sets using L-fuzzy rough grills. We introduce the concept of Čech L-fuzzy rough G-proximity spaces and L-fuzzy rough G-LO-proximity spaces. On this new nearness structure, we prove some basic topological results and to support the proposed approach, examples are given. [ABSTRACT FROM AUTHOR]

Summary: Hierarchical classification learning aims to exploit the hierarchical relationship between data categories. The high dimensionality and dynamic of the data feature space are the main challenges of this research. Hierarchical feature selection uses a hierarchical structure to divide large‐scale tasks into multiple small tasks, which can more effectively improve the training speed and prediction accuracy of classification models. To present, existing online feature selection methods ignore the hierarchical structure of data. In addition, the dependency relationships in the hierarchical structure can serve as auxiliary knowledge to aid feature selection. Based on this, this paper proposes an online streaming hierarchical feature selection method based on kernelized fuzzy rough sets (OFS‐HNFRS). First, we use the prior knowledge of the hierarchical structure to divide the sample set into multiple subsets. Second, the dependency relationship of hierarchical structure is extended to kernelized fuzzy rough sets, and hierarchical category dependency based on kernelized fuzzy rough sets is defined. Finally, a new online feature selection framework is proposed, which is used to evaluate the relevance, significance, and redundancy of features. We verify the effectiveness of the proposed algorithm on six hierarchical datasets and eight flat datasets. [ABSTRACT FROM AUTHOR]

In this paper, by integrating the advantages of multi-granulation fuzzy rough set (MGFRS) and variable precision fuzzy rough set (VPFRS), we introduce a new model of multi-granulation granular variable precision fuzzy rough set (MGVPFRS) and study its basic properties. The new model includes three basic models: optimistic, pessimistic, and compromise variable precision model, so it has better tolerance and diversity. Later, by combining MGVPFRS with traditional TOPSIS method, we propose a novel multi-criteria decision-making method (MCDM). In which, MGVPFRS is applicable to acquire criterion weights. This acquisition method is more convincing, because the criteria weights are computed, not given by experts. Finally, we apply our novel method to the challenging bone graft selection problem. Through a series of experiments and comparative analysis, the validity and reliability of our model are tested. In particular, the three strategies of optimism, pessimism, and compromise enable doctors and patients to make decisions according to their personal preferences. [ABSTRACT FROM AUTHOR]

In order to define the concept of nearness between L -fuzzy rough sets, we introduce the concept of Čech L -fuzzy rough proximity spaces. On this new nearness structure, we prove some basic topological results. To support the proposed approach, examples are given. Further, we introduce L -fuzzy rough grills, L -fuzzy rough filters and L -fuzzy rough clusters and on Čech L -fuzzy rough proximity spaces, and find the relationship between them. [ABSTRACT FROM AUTHOR]

Understanding household energy-consumption patterns is essential for developing effective energy-conservation strategies. This study aims to identify 'out-profiled' consumers—households that exhibit atypical energy-usage behaviors—by applying four distinct feature-selection methodologies. Specifically, we utilized the chi-square independence test to assess feature independence, recursive feature elimination with multinomial logistic regression (RFE-MLR) to identify optimal feature subsets, random forest (RF) to determine feature importance, and a combined fuzzy rough feature selection with fuzzy rough nearest neighbors (FRFS-FRNN) for handling uncertainty and imprecision in data. These methods were applied to a dataset based on a survey of 383 households in Brazil, capturing various factors such as household size, income levels, geographical location, and appliance usage. Our analysis revealed that key features such as the number of people in the household, heating and air conditioning usage, and income levels significantly influence energy consumption. The novelty of our work lies in the comprehensive application of these advanced feature-selection techniques to identify atypical consumption patterns in a specific regional context. The results showed that households without heating and air conditioning equipment in medium- or high-consumption profiles, and those with lower- or medium-income levels in medium- or high-consumption profiles, were considered out-profiled. These findings provide actionable insights for energy providers and policymakers, enabling the design of targeted energy-conservation strategies. This study demonstrates the importance of tailored approaches in promoting sustainable energy consumption and highlights notable deviations in energy-use patterns, offering a foundation for future research and policy development. [ABSTRACT FROM AUTHOR]

How to effectively handle imbalanced data is one of the hot issues in the fields of machine learning and data mining. Undersampling is a popular technique of dealing with imbalanced data. The aim of undersampling is to select an instance subset from the majority class of an imbalanced dataset and then make the dataset balanced. However, the traditional undersampling approaches may lead to the information loss of majority class instances. Therefore, on the basis of the concept of the importance degree of a fuzzy granule, a measure criterion of selecting representative instances from the majority class is presented in this paper by considering the fuzzy relations between the k-nearest neighbors of a majority class instance and the minority class instances. Then, we put forward an undersampling approach based on fuzzy rough sets (USFRS). With the proposed USFRS, the representativeness of the selected majority class instances can be guaranteed and the information loss due to undersampling can be reduced to the utmost extent. Furthermore, USFRS is compared with the relative undersampling methods, and the difference of the experimental results is analyzed by the statistic test. The experimental results demonstrate that USFRS performs well in classification for imbalanced data. [ABSTRACT FROM AUTHOR]

Multi-label feature selection aims to mitigate the curse of dimensionality in multi-label data by selecting a smaller subset of features from the original set for classification. Existing multi-label feature selection algorithms frequently neglect the inherent uncertainty in multi-label data and fail to adequately consider the relationships between features and labels when assessing the importance of features. In response to this challenge, a Fuzzy Information Gain Ratio-based multi-label feature selection considering Label Correlation (FIGR_LC) algorithm is proposed. FIGR_LC evaluates feature importance by combining the relationship between features and individual labels, as well as the correlation between features and label sets. Subsequently, a feature ranking is established based on these feature weights. Experimental results substantiate the effectiveness of FIGR_LC, showcasing its superiority over several established feature selection methods. [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.)

Fuzzy rough set theory gives a mathematical tool for studying unsettled knowledge that is beclouded, inexact, and mutually exclusive. The perception and conclusions of fuzzy rough sets theory are inextricably linked to topological perception. The topological appearance and its applications in fuzzy rough sets theory have been extensively discussed by researchers. The underlying subordinate of topology and classic fuzzy rough sets theory, as well as the expressive work done in this area over the previous years, are highlighted in this research. [ABSTRACT FROM AUTHOR]

In this paper, we study the concept of nearness between L-fuzzy rough sets and the concepts of L-fuzzy rough grills and L-fuzzy rough δℱ-clusters on a Čech L-fuzzy rough proximity space (X,δℱ). Also, we investigate the relationship among L-fuzzy rough grills, L-fuzzy rough δℱ-clan and L-fuzzy rough δℱ-clusters. Moreover, we introduce the L-fuzzy rough LO-proximity spaces and study some of its basic properties. An adequate number of examples support the study. [ABSTRACT FROM AUTHOR]

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 the era of big data, the amount of class labels is growing rapidly, which poses a great challenge to classification tasks. The hierarchical classification was thus introduced to address this issue by considering the structural information between different class labels. In this paper, we propose an incremental feature selection algorithm for handling the arrival of new samples by using the theory of fuzzy rough sets. As a preliminary step, we propose a non-incremental hierarchical feature selection algorithm, which is an improved version of the existing method. Then utilizing the sibling strategy, the incremental calculation of the dependency degree at the arrival of samples is discussed. Based on the analysis of dependency degree change, we design feature addition and deletion strategies, as well as the incremental feature selection algorithm. In the experimental section, two versions of algorithms are designed. The experimental results show that our improvement of the existing method is highly effective and can significantly accelerate the process of feature selection. In addition, version 2 of the incremental algorithm exhibits much higher efficiency than the improved non-incremental algorithm on several datasets, as well as the existing method. Compared to six hierarchical feature selection algorithms, our algorithm achieves better results on the classification accuracy and three hierarchical evaluation metrics. The effectiveness and efficiency of version 1 are also verified by the comparison of version 2 and other results. [ABSTRACT FROM AUTHOR]

In the big data era, the sample size is becoming increasingly large, the data dimensionality is also becoming extremely high, moreover, there exists hierarchical structure between different class labels. This paper investigates incremental feature selection for hierarchical classification based on the dependency degree of inclusive strategy and solves the hierarchical classification problem where labels are distributed at arbitrary nodes in tree structure. Firstly, the inclusive strategy is used to reduce the negative sample space by exploiting the hierarchical label structure. Secondly, a new fuzzy rough set model is introduced based on inclusive strategy, and a dependency calculation algorithm based on the inclusive strategy and a non-incremental feature selection algorithm are also proposed. Then, the dependency degree based on the inclusive strategy is proposed by adopting the incremental mechanism. Based on these, two incremental feature selection frameworks based on two strategies are designed. Lastly, a comparative study with the method based on the sibling strategy is performed. The?feasibility?and?efficiency?of the proposed algorithms are verified by numerical experiments.

Aiming to optimize the diagnosis of pulmonary embolism (PE) in patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD), we conducted a retrospective study enrolling 185 AECOPD patients, of whom 90 were diagnosed with PE based on computed tomography pulmonary angiography (CTPA). Ten characteristic indicators and 27 blood indicators were extracted for each patient. First, we quantified the importance of each indicator for diagnosing PE in AECOPD using fuzzy rough sets (FRS) and selected the more important indicators to construct a support vector machine (SVM) diagnosis model called FRS-SVM. The performance of the proposed diagnosis model on the test sets was compared to that of the logistic regression model. The average accuracy and area under the curve (AUC) of the proposed model for the test sets in 10 independent trials were 94.67% and 0.944, respectively, compared to 80.41% and 0.809 for the logistic regression model. Thus, we validated the higher accuracy and stability of the FRS-SVM for PE diagnosis in patients with AECOPD. This model improved the prediction probability before CTPA and can be used in clinical practice to help doctors make decisions.

The foundation of textile machinery digitization and intelligence is condition monitoring and identification. Online condition monitoring of looms is of great significance to ensure their long-term stable operation and improve their digital management level. However, the existing loom condition monitoring methods have problems such as insufficient depth of information mining, low condition recognition rate and poor system versatility. As a result, the loom on-board condition monitoring technology based on fuzzy rough set and improved DSmT theory is studied. To begin, we examine the loom operation mechanism, loom state characterization, and loom state feature data composition. Then, using the fuzzy rough set method, we analyze and make decisions on the loom state feature data, apply the theory of uncertainty and importance improvement DSmT fusion to solve the uncertainty problem of the rough set method's decision rules, and build the loom state feature decision network on the embedded terminal using the decision rules. Meanwhile, to collect, communicate, display, and alarm loom characteristic data, this paper employs the STM32F407ZET6 microcontroller and designs a loom system status data collection platform with the AD7730 as the core, as well as tests loom status monitoring data collection and loom status data analysis and decision method based on this platform. The experimental findings show the usefulness of attribute data gathering as well as data analysis and decision-making processes. The technology enhances the precision of loom condition identification and decision making, as well as the safety and quality of manufacturing. It is critical for carrying out applications like as problem detection, remote monitoring, efficiency optimization, and intelligent weaving machine management. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Frontiers of Computer Science & Technology 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.)

The foundation of textile machinery digitization and intelligence is condition monitoring and identification. Online condition monitoring of looms is of great significance to ensure their long-term stable operation and improve their digital management level. However, the existing loom condition monitoring methods have problems such as insufficient depth of information mining, low condition recognition rate and poor system versatility. As a result, the loom on-board condition monitoring technology based on fuzzy rough set and improved DSmT theory is studied. To begin, we examine the loom operation mechanism, loom state characterization, and loom state feature data composition. Then, using the fuzzy rough set method, we analyze and make decisions on the loom state feature data, apply the theory of uncertainty and importance improvement DSmT fusion to solve the uncertainty problem of the rough set method's decision rules, and build the loom state feature decision network on the embedded terminal using the decision rules. Meanwhile, to collect, communicate, display, and alarm loom characteristic data, this paper employs the STM32F407ZET6 microcontroller and designs a loom system status data collection platform with the AD7730 as the core, as well as tests loom status monitoring data collection and loom status data analysis and decision method based on this platform. The experimental findings show the usefulness of attribute data gathering as well as data analysis and decision-making processes. The technology enhances the precision of loom condition identification and decision making, as well as the safety and quality of manufacturing. It is critical for carrying out applications like as problem detection, remote monitoring, efficiency optimization, and intelligent weaving machine management. [ABSTRACT FROM AUTHOR]

Axiomatization is a lively research direction in fuzzy rough set theory. Fuzzy variable precision rough set (FVPRS) incorporates fault-tolerant factors to fuzzy rough set, so its axiomatic description becomes more complicated and difficult to realize. In this paper, we present an axiomatic approach to FVPRSs based on residuated lattice (L-fuzzy variable precision rough set (LFVPRS)). First, a pair of mappings with three axioms is utilized to characterize the upper (resp., lower) approximation operator of LFVPRS. This is distinct from the characterization on upper (resp., lower) approximation operator of fuzzy rough set, which consists of one mapping with two axioms. Second, utilizing the notion of correlation degree (resp., subset degree) of fuzzy sets, three characteristic axioms are grouped into a single axiom. At last, various special LFVPRS generated by reflexive, symmetric and transitive L-fuzzy relation and their composition are also characterized by axiomatic set and single axiom, respectively. [ABSTRACT FROM AUTHOR]

Incremental datasets are becoming increasingly common as interesting data are continually accumulated across various application fields. Selecting informative attributes from dynamically changing datasets poses numerous challenges. Completely reapplying the attribute reduction algorithm to detect the changes in the data and learn the selected attributes following frequently changing data is prohibitively expensive. In this regard, an incremental processing mechanism is desired to facilitate progressively updating the attribute reducts when the data is updated. In this paper, we consider the maintenance of the fuzzy rough attribute reduction in dynamic data that is changing through the arrival of samples. Based on the transformation of attribute reduction in a fuzzy decision system into the minimal transversal of a derivative hypergraph, a novel dynamic fuzzy rough attribute reduction approach is presented from a graph-theoretic perspective, so as to facilitate efficient computation of reduct in incremental datasets. Extensive experimental evaluation shows that the proposed dynamic graph-based fuzzy rough approach provides significantly faster attribute reduction than completely re-reduction by its original static counterpart as well as the existing dynamic attribute reduction approach based on fuzzy discernibility matrix, and is also effective in preserving the quality of the selected reduct. [ABSTRACT FROM AUTHOR]

Understanding household energy-consumption patterns is essential for developing effective energy-conservation strategies. This study aims to identify ‘out-profiled’ consumers—households that exhibit atypical energy-usage behaviors—by applying four distinct feature-selection methodologies. Specifically, we utilized the chi-square independence test to assess feature independence, recursive feature elimination with multinomial logistic regression (RFE-MLR) to identify optimal feature subsets, random forest (RF) to determine feature importance, and a combined fuzzy rough feature selection with fuzzy rough nearest neighbors (FRFS-FRNN) for handling uncertainty and imprecision in data. These methods were applied to a dataset based on a survey of 383 households in Brazil, capturing various factors such as household size, income levels, geographical location, and appliance usage. Our analysis revealed that key features such as the number of people in the household, heating and air conditioning usage, and income levels significantly influence energy consumption. The novelty of our work lies in the comprehensive application of these advanced feature-selection techniques to identify atypical consumption patterns in a specific regional context. The results showed that households without heating and air conditioning equipment in medium- or high-consumption profiles, and those with lower- or medium-income levels in medium- or high-consumption profiles, were considered out-profiled. These findings provide actionable insights for energy providers and policymakers, enabling the design of targeted energy-conservation strategies. This study demonstrates the importance of tailored approaches in promoting sustainable energy consumption and highlights notable deviations in energy-use patterns, offering a foundation for future research and policy development.

Aiming at the problem that manufacturing enterprises that rely more on asset projects currently lack effective means of divestiture of non performing assets, starting from incomplete information theory, this paper derives an optimal decision-making model for the divestiture path of non performing assets caused by the quality and safety crisis of manufacturing enterprises in an incomplete information environment, and uses the comprehensive method of SUMDII to establish a comprehensive integrated optimization model that reflects subjective evaluation and objective information. In addition, this study provides specific decision-making methods and implementation steps for optimizing the stripping path of non-performing assets. The empirical analysis results verify and demonstrate the feasibility, operability, accuracy, and applicability of the established model. The results show that the model designed in the study exhibits strong stability in sensitivity testing. When the parameter vectors are taken as (1,0,0,0), (1,1,0,0), (1,1,0,0), and (1,1,1,1), respectively, the ranking results corresponding to the first three parameter vectors are stable, all of which are A2 > A4 > A1 > A3. At the same time, the applied decision-making result of the model is A4 > A1 > A2 > A3, which is consistent with the best scheme evaluated by experts and superior to most comparative models. At the same time, in the analysis of decision-making characteristics, the research and design model has the most comprehensive review of decision-making elements, which is superior to other comparative models. It can be seen that the model designed by the research can lead to higher quality NPL divestiture schemes, which can help manufacturing enterprises improve asset quality and weaken the negative impact of the quality and safety crisis in manufacturing enterprises. [ABSTRACT FROM AUTHOR]

The evolving customer demands have significantly influenced the operational landscape of agricultural companies, including the transformation of their supply chains. As a response, many organizations are increasingly adopting green supply chain practices. This paper focuses on the initial step of selecting a green supplier, using the case study of the Semberka Company. The objective is to align the company with customer requirements and market trends. Expert decision making, grounded in linguistic values, was employed to facilitate the transformation of these values into fuzzy numbers and subsequently derive rough number boundaries. Ten economic-environmental criteria were identified, and six suppliers were evaluated against these criteria. The fuzzy rough LMAW (Logarithm Methodology of Additive Weights) method was employed to determine the criteria weights, with emphasis placed on the quality criterion. The fuzzy rough MABAC (Multi-Attributive Border Approximation Area Comparison) method was then utilized to rank the suppliers and identify the top performer. The validity of the results was established through validation techniques and sensitivity analysis. This research contributes a novel approach to green supplier selection, employing the powerful tool of fuzzy rough sets. The flexible nature of this approach suggests its potential application in future investigations. The limitation of this study is more complicated calculations for the decision maker. However, this approach is adapted to human thinking and minimizes ambiguity and uncertainty in decision making, and in future research, it is necessary to combine this approach with other methods of multi-criteria analysis. [ABSTRACT FROM AUTHOR]

The comparable property (or inclusion property) is the basic property of rough set theory. Unfortunately, the well-known granular variable precision fuzzy rough set does not satisfy the comparable property. To remedy this gap, a novel granular variable precision fuzzy rough set model with comparable property is proposed and discussed. Firstly, by analyzing the existing granular precision fuzzy rough set models, we find the reason that they do not satisfy the comparable property. Then, we give a new granular precision fuzzy rough set model with the comparable property. Secondly, we study the basic properties, characterization theorem and accuracy measure of the new model. Thirdly, we apply the novel model in the study of fuzzy decision system. An attribute reduction method is developed, and the corresponding attribute reduction algorithm is designed. Finally, by using public accessible datasets, we make a series of experiments to verify the effectiveness and reliability of our method and make comparisons with some existing attribute reduction methods. [ABSTRACT FROM AUTHOR]

Human emotion label prediction is crucial to Artificial Intelligent in the Internet of Things (IoT). Facial expression recognition is the main technique to predict human emotion labels. Existing facial expression recognition methods do not consider the compound emotion and the fuzziness of emotion labels. Fuzzy learning is a mathematical tool for dealing with fuzziness and uncertainty information. The advantage of using fuzzy learning for human emotion recognition is that multiple fuzzy sentiment labels can be processed simultaneously. This paper proposes a fuzzy learning-based expression recognition method for human emotion label prediction. First, a fuzzy label distribution system is constructed using fuzzy sets for representing facial expressions. Then, two fuzzy label distribution prediction methods based on fuzzy rough sets are proposed to solve the compound emotion prediction. The probability that a sample is likely and definitely belongs to an emotion is obtained by calculating the upper and lower approximations. Experiments show the proposed algorithm not only performs well on human emotion label prediction but can also be used for other label distribution prediction tasks. The proposed method is more accurate and more general than other methods. The improvement of the method on the effect of emotion recognition extends the application scope of artificial intelligence in IoT. [ABSTRACT FROM AUTHOR]

The paper presents and evaluates an approach based on Rough Set Theory, and some variants and extensions of this theory, to analyze phenomena related to Information Disorder. The main concepts and constructs of Rough Set Theory, such as lower and upper approximations of a target set, indiscernibility and neighborhood binary relations, are used to model and reason on groups of social media users and sets of information that circulate in the social media. Information theoretic measures, such as roughness and entropy, are used to evaluate two concepts, Complexity and Milestone, that have been borrowed by system theory and contextualized for Information Disorder. The novelty of the results presented in this paper relates to the adoption of Rough Set Theory constructs and operators in this new and unexplored field of investigation and, specifically, to model key elements of Information Disorder, such as the message and the interpreters, and reason on the evolutionary dynamics of these elements. The added value of using these measures is an increase in the ability to interpret the effects of Information Disorder, due to the circulation of news, as the ratio between the cardinality of lower and upper approximations of a Rough Set, cardinality variations of parts, increase in their fragmentation or cohesion. Such improved interpretative ability can be beneficial to social media analysts and providers. Four algorithms based on Rough Set Theory and some variants or extensions are used to evaluate the results in a case study built with real data used to contrast disinformation for COVID-19. The achieved results allow to understand the superiority of the approaches based on Fuzzy Rough Sets for the interpretation of our phenomenon. [ABSTRACT FROM AUTHOR]

We propose a machine-learning approach for Forex prices that forecasts trends in terms of whether or not the closing price will change for more than a threshold and whether that change is an increase or a decrease. Instead of using the prices as such, we carry out the forecast solely in terms of indicators that are popular among small-scale traders; our goal is to determine whether these convey sufficient information for a precise forecast for different change thresholds and horizons. Fuzzy rough sets are used to represent and select among multiple economic indicators and to construct a classifier to forecast price changes. High-quality forecasts are feasible for short horizons and for small thresholds of change for all fifteen currency pairs studied in the experiments. [ABSTRACT FROM AUTHOR]

Fine-grained sentiment analysis, known as Aspect-Based Sentiment Analysis (ABSA), establishes the polarity of a section of text concerning a particular aspect. Aspect, sentiment, and emotion categorisation are the three steps that make up the configuration of ABSA, which we looked into for the dataset of English reviews. In this work, due to the fuzzy nature of textual data, we investigated machine learning methods based on fuzzy rough sets, which we believe are more interpretable than complex state-of-the-art models. The novelty of this paper is the use of a pipeline that incorporates all three mentioned steps and applies Fuzzy-Rough Nearest Neighbour classification techniques with their extension based on ordered weighted average operators (FRNN-OWA), combined with text embeddings based on transformers. After some improvements in the pipeline's stages, such as using two separate models for emotion detection, we obtain the correct results for the majority of test instances (up to 81.4%) for all three classification tasks. We consider three different options for the pipeline. In two of them, all three classification tasks are performed consecutively, reducing data at each step to retain only correct predictions, while the third option performs each step independently. This solution allows us to examine the prediction results after each step and spot certain patterns. We used it for an error analysis that enables us, for each test instance, to identify the neighbouring training samples and demonstrate that our methods can extract useful patterns from the data. Finally, we compare our results with another paper that performed the same ABSA classification for the Dutch version of the dataset and conclude that our results are in line with theirs or even slightly better. [ABSTRACT FROM AUTHOR]

Aiming to optimize the diagnosis of pulmonary embolism (PE) in patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD), we conducted a retrospective study enrolling 185 AECOPD patients, of whom 90 were diagnosed with PE based on computed tomography pulmonary angiography (CTPA). Ten characteristic indicators and 27 blood indicators were extracted for each patient. First, we quantified the importance of each indicator for diagnosing PE in AECOPD using fuzzy rough sets (FRS) and selected the more important indicators to construct a support vector machine (SVM) diagnosis model called FRS-SVM. The performance of the proposed diagnosis model on the test sets was compared to that of the logistic regression model. The average accuracy and area under the curve (AUC) of the proposed model for the test sets in 10 independent trials were 94.67% and 0.944, respectively, compared to 80.41% and 0.809 for the logistic regression model. Thus, we validated the higher accuracy and stability of the FRS-SVM for PE diagnosis in patients with AECOPD. This model improved the prediction probability before CTPA and can be used in clinical practice to help doctors make decisions. [ABSTRACT FROM AUTHOR]

An automated retinal disease detection system has long been in existence and it provides a safe, no-contact and cost-effective solution for detecting this disease. This paper presents a game theory-based dynamic weighted ensemble of a feature extraction-based machine learning model and a deep transfer learning model for automatic retinal disease detection. The feature extractionbased machine learning model uses Gaussian kernel-based fuzzy rough sets for reduction of features, and XGBoost classifier for the classification. The transfer learning model uses VGG16 or ResNet50 or Inception-ResNet-v2. A novel ensemble classifier based on the game theory approach is proposed for the fusion of the outputs of the transfer learning model and the XGBoost classifier model. The ensemble approach significantly improves the accuracy of retinal disease prediction and results in an excellent performance when compared to the individual deep learning and feature-based models. [ABSTRACT FROM AUTHOR]

Natural Language processing (NLP) derives its roots from artificial intelligence and computational linguistics. The proliferation of large-scale web corpora and social media data as well as advances in machine learning and deep learning have led to practical applications in diverse NLP areas such as machine translation, information extraction, named entity recognition (NER), text summarization and sentiment analysis. Named-entity recognition (NER), is a sub task of information extraction that seeks to discover and categorize specific entities such as nouns or relations in unstructured text. In this paper, we present a review of the foundations three tolerance-based granular computing methods (rough sets, fuzzy-rough sets and near sets) for representing structured (documents) and unstructured (linguistic entities) text. Applications of these methods are presented via semi-supervised and supervised learning algorithms in labelling relational facts from web corpora and sentiment classification (non-topic based text). The performance of the three presented algorithms is discussed in terms of bench marked datasets and algorithms. We make the case that tolerance relations provide an ideal framework for studying the concept of similarity for text-based applications. The aim of our work is to demonstrate that approximation structures viewed through the prism of tolerance have a great deal of fluidity and integrate conceptual structures at different levels of granularity thereby facilitating learning in the presented NLP applications. [ABSTRACT FROM AUTHOR]

Outlier detection aims to find objects that behave differently from the majority of the data. Semi-supervised detection methods can utilize the supervision of partial labels, thus reducing false positive rates. However, most of the current semi-supervised methods focus on numerical data and neglect the heterogeneity of data information. In this paper, we propose a consistency-guided outlier detection algorithm (COD) for heterogeneous data with the fuzzy rough set theory in a semi-supervised manner. First, a few labeled outliers are leveraged to construct label-informed fuzzy similarity relations. Next, the consistency of the fuzzy decision system is introduced to evaluate attributes' contributions to knowledge classification. Subsequently, we define the outlier factor based on the fuzzy similarity class and predict outliers by integrating the classification consistency and the outlier factor. The proposed algorithm is extensively evaluated on 20 freshly proposed datasets. Experimental results demonstrate that COD is better than or comparable with the leading outlier detectors. • A label-informed fuzzy similarity relation is introduced to model heterogeneous data. • Classification consistency is introduced as a metric to assess attribute importance. • A fuzzy rough sets-based detection model with a semi-supervised approach is proposed. • Extensive experiments demonstrate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Granular structures are mathematical representations of knowledge used in granular computing. As a new type of granular structure, fuzzy β -covering has attracted widespread attention in recent years. One of the most critical related studies is measuring the uncertainty of fuzzy β -coverings. It is the foundation for studying fuzzy β -covering applications, such as classification and clustering. In this paper, we investigate the uncertainty measures of fuzzy β -coverings from the viewpoints of algebra and information theory, and propose the corresponding methods of fuzzy β -covering reduction for feature selection. Firstly, a generalized fuzzy β -neighborhood and the associated fuzzy β -covering rough sets are presented. Secondly, three types of accuracy measures of fuzzy β -coverings are constructed via fuzzy β -covering rough sets. On this basis, the self-information of fuzzy β -coverings is defined by fusing accuracy and roughness measures. Based on the proposed uncertainty measures, two heuristic methods of fuzzy β -covering reduction are put forward and the corresponding algorithms are designed for monotonic feature selection. Finally, the performance of the proposed methods is compared with several mainstream feature selection methods and experimental results demonstrate the rationality and superiority of our proposed methods. • The concept of generalized fuzzy β -neighborhood is introduced. • Multi-fuzzy β -covering fusion-based accuracy and self-information are proposed. • Fuzzy dependency function and fuzzy self-information are defined. • Two monotonic fuzzy β -covering reduction methods are proposed. [ABSTRACT FROM AUTHOR]

Aiming at the situation that fuzzy condition attribute values in multi-scale decision table have dominance relations and the decision attribute values are fuzzy, we establish dominance-based fuzzy rough set model (DFRS) in multi-scale decision table (MSDT). In order to investigate the knowledge acquisition efficiency of DFRS in MSDT, we give the optimal scale selection and reduction method to obtain all the optimal scales and all the optimal scale reducts. Besides, we also propose a simple algorithm to obtain an optimal scale reduct. Finally, we verify the effectiveness and practicability of our method through an example of information system security audit risk judgment and a comparative experiment. Experimental results show that our method has obviously improved the knowledge acquisition efficiency compared with the traditional dominance-based fuzzy rough set and effectively integrates the optimal scale selection of the multi-scale decision table with attribute reduction. [ABSTRACT FROM AUTHOR]

The evolving customer demands have significantly influenced the operational landscape of agricultural companies, including the transformation of their supply chains. As a response, many organizations are increasingly adopting green supply chain practices. This paper focuses on the initial step of selecting a green supplier, using the case study of the Semberka Company. The objective is to align the company with customer requirements and market trends. Expert decision making, grounded in linguistic values, was employed to facilitate the transformation of these values into fuzzy numbers and subsequently derive rough number boundaries. Ten economic-environmental criteria were identified, and six suppliers were evaluated against these criteria. The fuzzy rough LMAW (Logarithm Methodology of Additive Weights) method was employed to determine the criteria weights, with emphasis placed on the quality criterion. The fuzzy rough MABAC (Multi-Attributive Border Approximation Area Comparison) method was then utilized to rank the suppliers and identify the top performer. The validity of the results was established through validation techniques and sensitivity analysis. This research contributes a novel approach to green supplier selection, employing the powerful tool of fuzzy rough sets. The flexible nature of this approach suggests its potential application in future investigations. The limitation of this study is more complicated calculations for the decision maker. However, this approach is adapted to human thinking and minimizes ambiguity and uncertainty in decision making, and in future research, it is necessary to combine this approach with other methods of multi-criteria analysis.

Human emotion label prediction is crucial to Artificial Intelligent in the Internet of Things (IoT). Facial expression recognition is the main technique to predict human emotion labels. Existing facial expression recognition methods do not consider the compound emotion and the fuzziness of emotion labels. Fuzzy learning is a mathematical tool for dealing with fuzziness and uncertainty information. The advantage of using fuzzy learning for human emotion recognition is that multiple fuzzy sentiment labels can be processed simultaneously. This paper proposes a fuzzy learning-based expression recognition method for human emotion label prediction. First, a fuzzy label distribution system is constructed using fuzzy sets for representing facial expressions. Then, two fuzzy label distribution prediction methods based on fuzzy rough sets are proposed to solve the compound emotion prediction. The probability that a sample is likely and definitely belongs to an emotion is obtained by calculating the upper and lower approximations. Experiments show the proposed algorithm not only performs well on human emotion label prediction but can also be used for other label distribution prediction tasks. The proposed method is more accurate and more general than other methods. The improvement of the method on the effect of emotion recognition extends the application scope of artificial intelligence in IoT.

The approximation learning of fuzzy concepts associated with fuzzy rough sets and three-way decisions is a useful technology for the representation, learning, and transformation of uncertain knowledge. However, how to integrate the advantages of fuzzy rough approximations and three-way approximations has rarely been carefully investigated so far. In this article, we focus on exploring the connection and interplay of these two methods, and further propose the hierarchical fuzzy rough approximations in the dynamic fuzzy open-world environment. We utilize the temporal-spatial perspectives of three-way decisions to construct multigranularity structures and implement multigranularity learning. The temporality of data and the spatiality of model parameters are both considered in such frameworks. Subsequently, we discuss the interpretation and representation of fuzzy three-way regions in fuzzy rough sets with some definitions and properties. The max, double, and aggregated evaluation-based models of three-way approximations are proposed to gradually transform a fuzzy concept to a crisp concept by the time-evolving attributes. Finally, the comparative experimental results between static and dynamic approaches demonstrate the effectiveness of our proposed hierarchical approximation learning models. [ABSTRACT FROM AUTHOR]

As a combination of fuzzy sets and covering rough sets, fuzzy $\beta $ covering has attracted much attention in recent years. The fuzzy $\beta $ neighborhood serves as the basic granulation unit of fuzzy $\beta $ covering. In this article, a new discernibility measure with respect to the fuzzy $\beta $ neighborhood is proposed to characterize the distinguishing ability of a fuzzy covering family. To this end, the parameterized fuzzy $\beta $ neighborhood is introduced to describe the similarity between samples, where the distinguishing ability of a given fuzzy covering family can be evaluated. Some variants of the discernibility measure, such as the joint discernibility measure, conditional discernibility measure, and mutual discernibility measure, are then presented to reflect the change of distinguishing ability caused by different fuzzy covering families. These measures have similar properties as the Shannon entropy. Finally, to deal with knowledge reduction with fuzzy $\beta $ covering, we formalize a new type of decision table, that is, fuzzy $\beta $ covering decision tables. The data reduction of fuzzy covering decision tables is addressed from the viewpoint of maintaining the distinguishing ability of a fuzzy covering family, and a forward attribute reduction algorithm is designed to reduce redundant fuzzy coverings. Extensive experiments show that the proposed method can effectively evaluate the uncertainty of different types of datasets and exhibit better performance in attribute reduction compared with some existing algorithms. [ABSTRACT FROM AUTHOR]

Feature evaluation is an important issue in constructing a feature selection algorithm in kernelized fuzzy rough sets, which has been proven to be an effective approach to deal with nonlinear classification tasks and uncertainty in learning problems. However, the feature evaluation function developed with kernelized fuzzy rough sets cannot better reflect the affinity relationship of samples and is time-consuming. To overcome these drawbacks, in this article, the problem of feature selection with kernelized fuzzy rough sets is studied based on the spectral graph theory. First, the within-class and between-class sample similarity matrices by using kernelized fuzzy approximation operators are constructed. Two operators, which can capture the affinity relationship of samples, are then introduced based on the sample similarity matrices. The proposed operator can be regarded as the sum of the weighted kernelized fuzzy approximation operators. Second, based on the ratio criterion, a feature evaluation function and its corresponding feature selection algorithm FRKF are presented, which can effectively evaluate the importance of features. Third, to illustrate the performance of the proposed algorithm, extensive experiments have been carried out to compare FRKF and other well-known feature selection methods, including the feature ranking methods and feature subset selection methods on various classification tasks. The experimental results on real-world datasets demonstrate that FRKF achieves the high performances in terms of the robustness, efficiency, and effectiveness. [ABSTRACT FROM AUTHOR]