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Edge computing is an emerging computing paradigm, which offers a great opportunity to implement data mining-based services and applications for a large number of devices and sensors in Internet of Things. However, the new paradigm is faced with security and privacy challenges due to the diversity and the limited capability of edge components. In particular, data privacy is one of the most concerned problems for all the participants. In this paper, we propose a framework of privacy-preserving data mining based on private random decision trees in edge computing, which not only gives the strong privacy guarantee, but also provides a certain amount of data utility. Firstly, we design a preservation framework to implement private random decision trees satisfying local differential privacy. Secondly, we present the concrete implementations of algorithms and the corresponding task that each participant needs to undertake. Thirdly, we analyze the key factors to influence privacy and utility, including the allocation of data and privacy budget. Fourthly, we give the improved algorithms to further increase the utility with strong privacy preservation. Finally, extensive experiments demonstrate the good performance of our designed framework. [ABSTRACT FROM AUTHOR]

Decision trees (DTs) are one of the most popular white-box machine-learning techniques. Traditionally, DTs are induced using a top-down greedy search that may lead to sub-optimal solutions. One of the emerging alternatives is an evolutionary induction inspired by the biological evolution. It searches for the tree structure and tests simultaneously, which results in less complex DTs with at least comparable prediction performance. However, the evolutionary search is computationally expensive, and its effective application to big data mining needs algorithmic and technological progress. In this paper, noting that many trees or their parts reappear during the evolution, we propose a reuse strategy. A fixed number of recently processed individuals (DTs) is stored in a so-called repository. A part of the repository entry (related to fitness calculations) is maintained on a CPU side to limit CPU/GPU memory transactions. The rest of the repository entry (tree structures) is located on a GPU side to speed up searching for similar DTs. As the most time-demanding task of the induction is the DTs' evaluation, the GPU first searches similar DTs in the repository for reuse. If it fails, the GPU has to evaluate DT from the ground up. Large artificial and real-life datasets and various repository strategies are tested. Results show that the concept of reusing information from previous generations can accelerate the original GPU-based solution further. It is especially visible for large-scale data. To give an idea of the overall acceleration scale, the proposed solution can process even billions of objects in a few hours on a single GPU workstation. [ABSTRACT FROM AUTHOR]

Decision tree algorithm is an important classification method in data mining techniques. A decision tree creates classification and regression models like a tree that has a root node, branches, and leaf nodes. Logistic regression which is an alternative method to regression analysis when the dependent variable is a dichotomy, is another technique used for classification purposes. Within the scope of this research, logistic regression, linear regression, classification tree, and regression tree were applied on the same data set. This study explores the most important variables determining the house price by using these four methods. Models’ performances and predictive powers were compared and the best model is determined. This comparison was performed using 414 real estate data on 5 independent variables and the dependent variable is house price. The findings showed that the classification tree model for real estate valuation data performs better than standard approaches. [ABSTRACT FROM AUTHOR]

Decision trees (DTs) are a widely used prediction tool, owing to their interpretability. Standard learning methods follow a locally optimal approach that trades off prediction performance for computational efficiency. Such methods can however be far from optimal, and it may pay off to spend more computational resources to increase performance. Monte Carlo tree search (MCTS) is an approach to approximate optimal choices in exponentially large search spaces. We propose a DT learning approach based on the Upper Confidence Bound applied to tree (UCT) algorithm, including procedures to expand and explore the space of DTs. To mitigate the computational cost of our method, we employ search pruning strategies that discard some branches of the search tree. The experiments show that proposed approach outperformed the C4.5 algorithm in 20 out of 31 datasets, with statistically significant improvements in the trade‐off between prediction performance and DT complexity. The approach improved locally optimal search for datasets with more than 1,000 instances, or for smaller datasets likely arising from complex distributions. This article is categorized under:Algorithmic Development > Hierarchies and TreesApplication Areas > Data Mining Software ToolsFundamental Concepts of Data and Knowledge > Data Concepts [ABSTRACT FROM AUTHOR]

Recently, the rise in student numbers has led to the establishment of many new colleges and universities in China. As a result, there has been a significant increase in data collection on students' athletic skills. To manage these data, educational institutions are implementing information management systems. However, tracking sports results remain challenging since sports information may not always be collected during sports teaching. This work presents a systematic strategy to address this problem by evaluating student athletes' abilities using an Apriori algorithm, decision tree (DT), and association rule. It covers the processes for collecting data, preprocessing, selecting features, and evaluating models. The efficiency of the DT algorithm in solving classification problems, including student achievement analysis, is emphasized. The association rule algorithm is applied to figure out the correlation between students' physical fitness and their involvement in physical education. The Apriori algorithm is introduced to reduce the amount of data needed in merging item sets. Lastly, the overall architecture of the college students' physical fitness analysis system is presented. It covers the insertion of sports test scores, the calculation of total scores, and the application of DT analysis for evaluating student achievements. The process involves standardizing database information, selecting a training instance set, and determining attributes based on information gain. The efficiency of the system is evaluated in terms of accuracy, precision, recall, and F1 score. In comparison with previous works, our recommended system can track and analyze students' athletic capability, fitness, and physical ability to create personalized workout routines and monitor their health in real-time. [ABSTRACT FROM AUTHOR]

Feature Selection (FS) is an essential research topic in the area of machine learning. FS, which is the process of identifying the relevant features and removing the irrelevant and redundant ones, is meant to deal with high dimensionality problems to select the best performing feature subset. In the literature, many feature selection techniques approach the task as a research problem, where each state in the search space is a possible feature subset. In this paper, we introduce a new feature selection method based on reinforcement learning. First, decision tree branches are used to traverse the search space. Second, a transition similarity measure is proposed so as to ensure exploit-explore trade-off. Finally, the informative features are the most involved ones in constructing the best branches. The performance of the proposed approaches is evaluated on nine standard benchmark datasets. The results using the AUC score show the effectiveness of the proposed system. [ABSTRACT FROM AUTHOR]

Telecom sector is hugely losing profits in different degrees due to various undesired classes of its customers. Churners, a certain class of customers shifting to the competitors, are the most undesired class of customers who are the predominant reason for the losses. Still, there are other classes of customers in this business who stay with the enterprise, but they are inactive in using the services and leading to uncertainty and an insignificant amount of profits. When data mining techniques are applied to such applications they produce customer models in the form of decision trees, etc. and provide customer's class label only such as churner/non-churner. Furthermore, they only focus on improving the technical interestingness measures of prediction models. Thus, very limited research has been carried out on turning the prediction results into useful decision making actions. Consequently, some manual work by domain expert has to be done to postprocess the model to obtain the actionable knowledge for changing the customer from undesired class to the desired one. However, some of the existing works are suggesting the actions to convert the class of the customer from one category to another, but they have limitations in that they do not generalize to more than two classes. In this paper, a novel algorithm, which aptly fits the multi-class setting of Telecom sector, is presented that suggest actions to change the customer from an undesired class to a desirable one with maximum net profit. We explain our proposed method with the help of a case study of the Telecom sector. Empirical tests are conducted on the case study problem and also on UCI benchmark data and shown that our method is effective and scalable. With the help of comparison with state-of-the-art methods and substantial experiments, we demonstrate the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

It is thought that data mining can solve many problems in visible light communication. In the VLC systems, different receiver and transmitter units are usually designed for each modulation technique. Here, the proposed systems are designed for shortened pulse position modulation. In the paper, K-Nearest Neighbor algorithm and Decision Trees based demodulators that can be applied to synchronous modulation techniques are proposed. Afterwards, a generalized entropy expression is obtained for M-SPPM signals. Additionally, demodulators that can decode signals according to the Manhattan and Minkowski distance and other demodulators are compared in terms of accuracy and bit error rate. From the numerical results, it can be seen that the proposed KNN and Decision Trees based demodulators can be used as traditional systems for synchronous modulation techniques. Furthermore, it is seen that bit error rate performances and accuracy rates of the proposed KNN based demodulator and decision trees based demodulator that is optimally designed are completely the same. In 4-SPPM signals, it is seen that the accuracy of KNN (Demodulator-1) and Decision Trees (Demodulator-3) is 98.78% for SNR=10 dB. Here, the accuracy of the KNN (Demodulator-2) and Decision Trees (Demodulator-5) is 99.07% for 8-SPPM signals. [ABSTRACT FROM AUTHOR]

Background: A decision tree is a crucial tool for describing the factors related to cardiovascular disease (CVD) risk and for predicting and explaining it for patients. Notably, the decision tree must be simplified because patients may have different primary topics or factors related to the CVD risk. Many decision trees can describe the data collected from multiple environmental heart disease risk datasets or a forest, where each tree describes the CVD risk for each primary topic. Methods: We demonstrate the presence of trees, or a forest, using an integrated CVD dataset obtained from multiple datasets. Moreover, we apply a novel method to an association-rule tree to discover each primary topic hidden within a dataset. To generalize the tree structure for descriptive tasks, each primary topic is a boundary node acting as a root node of a C4.5 tree with the least prodigality for the tree structure (PTS). All trees are assigned to a descriptive forest describing the CVD risks in a dataset. A descriptive forest is used to describe each CVD patient's primary risk topics and related factors. We describe eight primary topics in a descriptive forest acquired from 918 records of a heart failure–prediction dataset with 11 features obtained from five datasets. We apply the proposed method to 253,680 records with 22 features from imbalanced classes of a heart disease health–indicators dataset. Results: The usability of the descriptive forest is demonstrated by a comparative study (on qualitative and quantitative tasks of the CVD-risk explanation) with a C4.5 tree generated from the same dataset but with the least PTS. The qualitative descriptive task confirms that compared to a single C4.5 tree, the descriptive forest is more flexible and can better describe the CVD risk, whereas the quantitative descriptive task confirms that it achieved higher coverage (recall) and correctness (accuracy and precision) and provided more detailed explanations. Additionally, for these tasks, the descriptive forest still outperforms the C4.5 tree. To reduce the problem of imbalanced classes, the ratio of classes in each subdataset generating each tree is investigated. Conclusion: The results provide confidence for using the descriptive forest. [ABSTRACT FROM AUTHOR]

Failure in compulsory subjects such as chemistry, calculus, physics, and basic control systems could hamper the graduation process of students. Thus, students must be successful in such obligatory courses. To address this issue, this study aims to predict student performance based on their learning outcomes using data mining techniques. In particular, we utilize decision tree (DT), k-nearest neighbor (KNN), support vector machine (SVM), and naive Bayes (NB) algorithms to predict student performance. The data for this study were gathered from the learning outcomes of students in the basic control systems course and subsequently modeled using binary and nine-level classifications. The experimental results showed that DT could perform better than KNN, SVM, and NB in the binary and nine-level classifications. Interestingly, the results of DT (i.e., the prediction values) are almost similar to those of the original values of the basic control systems course. [ABSTRACT FROM AUTHOR]

In this article we have called "Decision trees" I studied in the first part their general characteristics, its structure and classification. In the second part of the article we model the process of launching a new product on the market using the decision tree. To make the tree, we first made a table with the likelihood of launching the product on the market, after which we made the decision tree and came to the answer I wanted to find out, that is, I learned that the company decides whether it wants to test the new product on the market. [ABSTRACT FROM AUTHOR]

The purpose of this study is to evaluate how learning disabilities (LDs), in combination with accommodations, affect the performance of a decision-tree to predict the stability of academic behaviour of undergraduate engineering students. Additionally, this study presents several examples to illustrate how a college could use the resultant model to choose the appropriate accommodation to give a student with a learning disability, from among a set of possible accommodations. The findings show that: (1) The models yield superior performance in predicting the stability category for a given student when the LD and accommodation factors are included; (2) Different types of accommodation action have different effects on the stability of academic behaviour, depending on the student pattern. Such a model could be useful for engineering faculties, as it would allow them to predict the stability of academic behaviour and to provide early intervention for students who are likely to need additional support. [ABSTRACT FROM AUTHOR]

Monotonic classification is a kind of classification task in which a monotonicity constraint exist between features and class, i.e., if sample $x_i$ has a higher value in each feature than sample $x_j$ , it should be assigned to a class with a higher level than the level of $x_j$ 's class. Several methods have been proposed, but they have some limits such as with limited kind of data or limited classification accuracy. In our former work, the classification accuracy on monotonic classification has been improved by fusing monotonic decision trees, but it always has a complex classification model. This work aims to find a monotonic classifier to process both nominal and numeric data by fusing complete monotonic decision trees. Through finding the completed feature subsets based on discernibility matrix on ordinal dataset, a set of monotonic decision trees can be obtained directly and automatically, on which the rank is still preserved. Fewer decision trees are needed, which will serve as base classifiers to construct a decision forest fused complete monotonic decision trees. The experiment results on 10 datasets demonstrate that the proposed method can reduce the number of base classifiers effectively and then simplify classification model, and obtain good classification performance simultaneously. [ABSTRACT FROM PUBLISHER]

Decision trees are decision support data mining tools that create, as the name suggests, a tree-like model. The classical C4.5 decision tree, based on the Shannon entropy, is a simple algorithm to calculate the gain ratio and then split the attributes based on this entropy measure. Tsallis and Renyi entropies (instead of Shannon) can be employed to generate a decision tree with better results. In practice, the entropic index parameter of these entropies is tuned to outperform the classical decision trees. However, this process is carried out by testing a range of values for a given database, which is time-consuming and unfeasible for massive data. This paper introduces a decision tree based on a two-parameter fractional Tsallis entropy. We propose a constructionist approach to the representation of databases as complex networks that enable us an efficient computation of the parameters of this entropy using the box-covering algorithm and renormalization of the complex network. The experimental results support the conclusion that the two-parameter fractional Tsallis entropy is a more sensitive measure than parametric Renyi, Tsallis, and Gini index precedents for a decision tree classifier. [ABSTRACT FROM AUTHOR]

Type II diabetes is one of the chronic diseases, which is the cause of death and disability in most of the countries. The objective of this research work is to apply particle swarm optimization (PSO) algorithm with improved operating parameters along with decision tree (C4.5) for accessing the risk factors for type II diabetes. The model developed with the consideration of type II diabetic risk has parameter values with normal and abnormal levels. Experimental analysis has been carried out using an improved combination of PSO with decision trees and its various splitting measures with real-world diabetic dataset. The improvement in PSO is made by proposing self-adaptive inertial weight with modified convergence logic for the particles to accelerate in the given search space. From the result analysis, it has been observed that the risk factors corresponding to diabetes are postprandial plasma glucose (PPG), glycosylated hemoglobin (A1c), mean blood glucose (MBG) and fasting plasma glucose (FPG) identified with an improved accuracy more than that of the existing methods and algorithms. The efficiency of prediction has been tested using Fisher's linear discriminant analysis. From the inference, it has been observed that there occurs a strong relationship between the risk factors such as PPG, A1c, MBG and FPG with the risk corresponding to type II diabetes. Hence, predictive analytics using an improved combination of PSO with decision trees can also be deployed for the identification of risk factors corresponding to other chronic diseases such as coronary heart disease and kidney disease. [ABSTRACT FROM AUTHOR]

Background: Allogenic hematopoietic stem cell transplantation is considered as an effective treatment for patients with acute myeloid leukemia. However, complications of transplantation, like aGVHD, affect the efficiency of allogenic hematopoietic stem cell transplantation. The present study aimed to implement different models of data mining (DM) (single and ensemble) for prediction of allogenic hematopoietic stem cell transplantation in patients with acute myeloid leukemia (transplantation against host disease). Method: We conducted this developmental study on 94 patients with 34 attributes in Taleghani Hospital, Tehran, Iran, during 2009--2017. In this practical study, data were analyzed via decision tree (DT) algorithms, including decision tree, random forest and gradient boosting (ensemble learning), artificial neural network (Single Learning), and support vector machine. Some criteria, like specificity, accuracy, Fmeasure, AUC (area under curve), and sensitivity, were reported in order to evaluate DT algorithms. Results: There were 34 transplantation-related variables; some predictors, such as liver, hemoglobin, and donor blood group, were found to be the most important ones. To predict aGVHD, the two selected algorithms included the most appropriate DM models, artificial neural network and support vector machine classifiers, with ROC of 100. Conclusion: This study indicated that DT algorithms could be successfully used for approving the efficiency of the models predicting allogenic hematopoietic stem cell transplantation. [ABSTRACT FROM AUTHOR]

The goal of this paper is to improve the predictive accuracy of data streaming algorithms without increasing the processing time of the incoming data. We propose the EnHAT (Ensemble Combined with Hoeffding Adaptive Tree) algorithm, which combines the state-of-the-art Hoeffding Adaptive Tree (HAT) algorithm with an ensemble of J48 decision trees induced from sequential chunks of the data stream. The slack time of HAT adaptation to a new window of incoming records is utilized in parallel for building a decision-tree ensemble. In our experiments on 4 benchmark streaming datasets and 4 synthetic datasets with different types of concept drift, EnHAT has reached the highest predictive accuracy in 23 out of 26 cases compared to an ensemble of J48 trees, HAT, and a single J48 model induced from the last sliding window. Thus, we can conclude that the ensemble/HAT synergy yields better prediction results than each one of the two approaches on its own. The higher accuracy does not come at the expense of any additional computational effort beyond the model induction times of the combined algorithms. • We build interpretable and accurate classification models from massive data streams. • The Hoeffding Adaptive Tree is combined with an ensemble of J48 decision trees. • The synergy between the two algorithms improves classification accuracy. • The proposed algorithm does not increase the model induction time. [ABSTRACT FROM AUTHOR]

In this paper, we deal with distributed data represented either as a finite set T of decision tables with equal sets of attributes or a finite set I of information systems with equal sets of attributes. In the former case, we discuss a way to the study decision trees common to all tables from the set T : building a decision table in which the set of decision trees coincides with the set of decision trees common to all tables from T. We show when we can build such a decision table and how to build it in a polynomial time. If we have such a table, we can apply various decision tree learning algorithms to it. We extend the considered approach to the study of test (reducts) and decision rules common to all tables from T. In the latter case, we discuss a way to study the association rules common to all information systems from the set I : building a joint information system for which the set of true association rules that are realizable for a given row ρ and have a given attribute a on the right-hand side coincides with the set of association rules that are true for all information systems from I , have the attribute a on the right-hand side, and are realizable for the row ρ. We then show how to build a joint information system in a polynomial time. When we build such an information system, we can apply various association rule learning algorithms to it. [ABSTRACT FROM AUTHOR]

Uncovering causal relationships in data is a major objective of data analytics. Currently, there is a need for scalable and automated methods for causal relationship exploration in data. Classification methods are fast and they could be practical substitutes for finding causal signals in data. However, classification methods are not designed for causal discovery and a classification method may find false causal signals and miss the true ones. In this paper, we develop a causal decision tree (CDT) where nodes have causal interpretations. Our method follows a well-established causal inference framework and makes use of a classic statistical test to establish the causal relationship between a predictor variable and the outcome variable. At the same time, by taking the advantages of normal decision trees, a CDT provides a compact graphical representation of the causal relationships, and the construction of a CDT is fast as a result of the divide and conquer strategy employed, making CDTs practical for representing and finding causal signals in large data sets. Experiment results demonstrate that CDTs can identify meaningful causal relationships and the CDT algorithm is scalable. [ABSTRACT FROM PUBLISHER]

Because clinical research is carried out in complex environments, prior domain knowledge, constraints, and expert knowledge can enhance the capabilities and performance of data mining. In this paper we propose an unexpected pattern mining model that uses decision trees to compare recovery rates of two different treatments, and to find patterns that contrast with the prior knowledge of domain users. In the proposed model we define interestingness measures to determine whether the patterns found are interesting to the domain. By applying the concept of domain-driven data mining, we repeatedly utilize decision trees and interestingness measures in a closed-loop, in-depth mining process to find unexpected and interesting patterns. We use retrospective data from transvaginal ultrasound-guided aspirations to show that the proposed model can successfully compare different treatments using a decision tree, which is a new usage of that tool. We believe that unexpected, interesting patterns may provide clinical researchers with different perspectives for future research. [ABSTRACT FROM AUTHOR]

Characterizing error structures in precipitation products not only facilitates their proper applications for scientific and practical purposes but also helps improve their retrieval algorithms and processing methods. Despite the fact that multiple precipitation products have been assessed in the literature, factors that affect their error structures remain inadequately addressed. By interpreting 60 binary decision trees, this study disentangles the error characteristics of precipitation products in terms of their spatiotemporal patterns and geographical factors. Three independent precipitation products - two satellite-based and one reanalysis datasets: the Integrated Multi-satellitE Retrievals for GPM (Global Precipitation Measurement) late run (IMERG-L), Soil Moisture to Rain-Advanced SCATterometer (SM2RAIN-ASCAT), and the Modern-Era Retrospective analysis for Research and Applications, Version 2 uncorrected precipitation output (MERRA2-UC), are evaluated across the contiguous United States from 2010 to 2019. The ground-based Stage IV precipitation dataset is used as the ground truth. Results indicate that the MERRA2-UC outperforms the IMERG-L and SM2RAIN-ASCAT with higher accuracy and more stable interannual patterns for the analysis period. Decision trees cross-assess three spatiotemporal factors and find that the underestimation of MERRA2-UC occurs in the east of the Rocky Mountains, and SM2RAIN-ASCAT underestimates precipitation over high latitudes, especially in winter. Additionally, the decision tree method ascribes system errors to nine different geographical characteristics, of which the distance to the coast, soil type, and DEM are the three dominant features. On the other hand, the land cover type, topography position index, and aspect are three relatively weak factors. • A decision tree (DT) method is proposed to evaluate precipitation product. • The DT method can disentangle multiple factors simultaneously. • 60 DTs are interpreted to disentangle errors in three precipitation products. • The distance to the coast, soil type, and DEM are highly related to product errors. [ABSTRACT FROM AUTHOR]

Due to the non-linear but highly interpretable representations, decision tree (DT) models have significantly attracted a lot of attention of researchers. However, it is difficult to understand and interpret DT models in ultrahigh dimensions and DT models usually suffer from the curse of dimensionality and achieve degenerated performance when there are many noisy features. To address these issues, this paper first presents a novel data-dependent generalization error bound for the perceptron decision tree (PDT), which provides the theoretical justification to learn a sparse linear hyperplane in each decision node and to prune the tree. Following our analysis, we introduce the notion of budget-aware classifier (BAC) with a budget constraint on the weight coefficients, and propose a supervised budgeted tree (SBT) algorithm to achieve non-linear prediction performance. To avoid generating an unstable and complicated decision tree and improve the generalization of the SBT, we present a pruning strategy by learning classifiers to minimize cross-validation errors on each BAC. To deal with ultrahigh label dimensions, based on three important phenomena of real-world data sets from a variety of application domains, we develop a sparse coding tree framework for multi-label annotation problems and provide the theoretical analysis. Extensive empirical studies verify that 1) SBT is easy to understand and interpret in ultrahigh dimensions and is more resilient to noisy features. 2) Compared with state-of-the-art algorithms, our proposed sparse coding tree framework is more efficient, yet accurate in ultrahigh label and feature dimensions. [ABSTRACT FROM AUTHOR]

Financial distress domain problem datasets are usually class imbalanced. In literature, data sampling is one of the widely used solutions to deal with the class imbalance problem. This article focuses on examining the data sampling effect on financial distress prediction models by single and ensemble learning techniques. The experimental datasets are based on three bankruptcy prediction and credit scoring datasets and twelve different single classifiers and classifier ensembles are constructed. We find that although some prediction models trained by the original class imbalanced datasets provide reasonable AUC, their type II errors are very high for the practical usage. However, when data sampling is performed over the datasets, all of the prediction models can slightly increase their AUC and largely reduce their type II errors. More specifically, the decision tree ensembles by bagging and boosting methods are the better choices for financial distress prediction. [ABSTRACT FROM AUTHOR]

Introduction: Many studies have been published to assess the best anthropometric measurements associated with cardiovascular diseases (CVDs), but controversies still exist. Aim: Investigating the association between CVDs and anthropometric measurements among Iranian adults. Methods: For a total population of 9354 aged 35 to 65, a prospective study was designed. Anthropometric measurements including ABSI (A Body Shape Index), Body Adiposity Index (BAI), Body Mass Index (BMI), Waist to Height Ratio (WHtR), Body Round Index (BRI), HC (Hip Circumference), Demispan, Mid-arm circumference (MAC), Waist-to-hip (WH) and Waist Circumference (WC) were completed. The association between these parameters and CVDs were assessed through logistic regression (LR) and decision tree (DT) models. Results: During the 6-year follow-up, 4596 individuals (49%) developed CVDs. According to the LR, age, BAI, BMI, Demispan, and BRI, in male and age, WC, BMI, and BAI in female had a significant association with CVDs (p-value < 0.03). Age and BRI for male and age and BMI for female represent the most appropriate estimates for CVDs (OR: 1.07, (95% CI: 1.06, 1.08), 1.36 (1.22, 1.51), 1.14 (1.13, 1.15), and 1.05 (1.02, 1.07), respectively). In the DT for male, those with BRI ≥ 3.87, age ≥ 46 years, and BMI ≥ 35.97 had the highest risk to develop CVDs (90%). Also, in the DT for female, those with age ≥ 54 years and WC ≥ 84 had the highest risk to develop CVDs (71%). Conclusion: BRI and age in male and age and BMI in female had the greatest association with CVDs. Also, BRI and BMI was the strongest indices for this prediction. [ABSTRACT FROM AUTHOR]

The presented study applies the methods of data mining and prediction models to the subject of memory functioning in developmental dyslexia. This article sets forth the results of an analysis of the decision tree algorithm for the classification of dyslexia/non-dyslexia, based on frequency data from the modified simplified conjoint recognition experiment-a paradigm based on the fuzzy-trace theory used to investigate verbatim and gist memory. This decision tree model was created with the use of the C&RT algorithm, which makes a prediction of the classification with the use of four predictors: the numbers of different types of answers depending on the specific stimuli presented. Seventy-one high school students, 33 with developmental dyslexia, took part in a memory experiment. The model created using the decision tree algorithm has a very good overall validity. Excellent developmental dyslexia classification was accompanied by satisfactory non-dyslexia classification. The decision tree proposed predictors that are supported both theoretically and empirically. The results obtained show an important role of verbatim and gist memory functioning in developmental dyslexia and suggest that the pattern of performance observed in the memory tests can be used as a predictor of the developmental dyslexia disorder. Results encourage further usage of decision trees. [ABSTRACT FROM AUTHOR]

The big data era has led to an exponential increase in data usage, resulting in significantly advancements in data-driven domains and data mining. However, due to privacy and regulatory requirements, sharing data among various institutions is not always possible. Federated learning can help address this problem, but existing studies that combine differential privacy with tree models have shown significant accuracy loss. In this study, we propose a Federated Differential Privacy Gradient Boosting Decision Tree (FDPBoost) that protects the private datasets of different owners while improving model accuracy. We select sensitive features according to the secure feature set indicator, and use an exponential mechanism to protect sensitive features and assign significant weight to the Laplace mechanism to protect leaf node values. Additionally, a distributed two-level boosting framework is designed to allocate the privacy budget between intra-iteration and inter-iteration decision trees while protecting model communication. The FDPBoost is tested on five datasets sourced from the materials and medical domains. Our experiments reveal that FDPBoost achieves competitive accuracy with traditional federated gradient boosting decision trees while also exhibiting a significant reduction in error rate as compared to PPGBDT (Zhao et al.) and FV-tree (Gao et al.). Notably, FDPBoost's error rate on the tumor-diagnosis dataset is 30% lower than that of FV-tree. [Display omitted] • A federated GBDT is designed to improve model accuracy and protect clients' privacy. • SFS indicator and significance weight are introduced to apply to differential privacy. • A distributed two-level boosting framework is proposed to effectively allocate privacy budget. • The efficiency of proposed model is verified on five datasets in professional fields. [ABSTRACT FROM AUTHOR]

In this paper, we investigated the utility of data mining to classify individuals into predefined categories of a target variable, based on their social and political attitude. Data collected for a social psychology study conducted in Greece in 1994 were used for this purpose. We established the theoretical background of our analysis through explanatory factor analysis. We ran the decision tree algorithm CHAID in order to build a predictive model that classifies the study participants in terms of their attitude toward physical and symbolic violence. The CHAID algorithm provided a decision tree that was easily interpreted, and which revealed meaningful predictive patterns. CHAID algorithm showed satisfactory predictive ability and promising alternatives to social psychology data analysis. To the best of our knowledge, there is no other evidence in the literature that the decision tree algorithms can be used to identify latent variables. [ABSTRACT FROM AUTHOR]

Highlights • A novel piloting decision recognition model for fuzziness and uncertainty problems. • Automatic acquisition and representation of the pilot's decision-making knowledge. • A flexible method that can mine the key factors which affect piloting decisions. • The standardization principle of piloting decision-making factors is proposed. • A feasibility basis for the realization of automatic smart ship piloting systems. Abstract With the further development of marine and information technologies, ship intelligence, green policies and automation will become mainstream with global cargo ships. Ship labor costs increase every year, so for the foreseeable future, the number of experienced crew members will be greatly reduced as smart ship emergence accelerates. At present, there is no mature research system for the human-like piloting of smart ships. In this paper, we use an improved decision tree, which could address problems of fuzziness and uncertainty. This will allow us to study the decision mechanisms of different piloting behaviors in order to realize the automatic acquisition and representation of the pilot's decision-making knowledge in inbound ship analysis as well as the simulated reproduction of the pilot's behavior. The simulation results show that the piloting decision recognition model, based on the fuzzy Iterative Dichotomiser 3 (ID3) decision tree, possesses a high reasoning speed and can accurately identify current piloting behavior. This provides theoretical guidance and a feasibility basis for research into human-like piloting behavior and the realization of automatic smart ship piloting systems. [ABSTRACT FROM AUTHOR]

Abstract: Decision-tree induction algorithms are widely used in knowledge discovery and data mining, specially in scenarios where model comprehensibility is desired. A variation of the traditional univariate approach is the so-called oblique decision tree, which allows multivariate tests in its non-terminal nodes. Oblique decision trees can model decision boundaries that are oblique to the attribute axes, whereas univariate trees can only perform axis-parallel splits. The vast majority of the oblique and univariate decision-tree induction algorithms employ a top-down strategy for growing the tree, relying on an impurity-based measure for splitting nodes. In this paper, we propose BUTIF—a novel Bottom-Up Oblique Decision-Tree Induction Framework. BUTIF does not rely on an impurity-measure for dividing nodes, since the data resulting from each split is known a priori. For generating the initial leaves of the tree and the splitting hyperplanes in its internal nodes, BUTIF allows the adoption of distinct clustering algorithms and binary classifiers, respectively. It is also capable of performing embedded feature selection, which may reduce the number of features in each hyperplane, thus improving model comprehension. Different from virtually every top-down decision-tree induction algorithm, BUTIF does not require the further execution of a pruning procedure in order to avoid overfitting, due to its bottom-up nature that does not overgrow the tree. We compare distinct instances of BUTIF to traditional univariate and oblique decision-tree induction algorithms. Empirical results show the effectiveness of the proposed framework. [Copyright &y& Elsevier]

When a public transport manager conducts a customer satisfaction survey (CSS), the goal is to determine the overall satisfaction of passengers with the service, as well as their satisfaction with specific aspects (e.g. frequency, speed, and comfort). Another fundamental objective is to assess the importance to customers of each attribute individually. Asking directly about this importance involves a number of drawbacks; therefore, most studies extract this importance from surveys that ask questions only about global satisfaction and specific satisfaction regarding each attribute. This paper investigates the capability and performance of two emerging data mining methods, namely, decision trees and neural networks, for extracting the importance of attributes from CSS. A total of 858 surveys about the metropolitan bus service in Granada (Spain) were used to model estimation and evaluation. The main advantages and disadvantages of each method are studied from the standpoint of public transport managers. [ABSTRACT FROM AUTHOR]

• Elemental profiles of 180 anchovy samples are determined by ICP-MS. • Influence of the production process on the element composition can be passed over. • Pivotal elements for origin discrimination are identified. • Element compositions provide highly specific fingerprint of anchovy products. • CHAID and QUEST decision tree algorithms are recommended for origin prediction. Quadrupole inductively coupled plasma mass spectrometry (Q-ICP-MS) and direct mercury analysis were used to determine the elemental composition of 180 transformed (salt-ripened) anchovies from three different fishing areas before and after packaging. To this purpose, four decision trees-based algorithms, corresponding to C5.0, classification and regression trees (CART), chi-square automatic interaction detection (CHAID), and quick unbiased efficient statistical tree (QUEST) were applied to the elemental datasets to find the most accurate data mining procedure to achieve the ultimate goal of fish origin prediction. Classification rules generated by the trained CHAID model optimally identified unlabelled testing bulk anchovies (93.9% F-score) by using just 6 out of 52 elements (As, K, P, Cd, Li, and Sr). The finished packaged product was better modelled by the QUEST algorithm which recognised the origin of anchovies with F-score of 97.7%, considering the information carried out by 5 elements (B, As, K. Cd, and Pd). Results obtained suggested that the traceability system in the fishery sector may be supported by simplified machine learning techniques applied to a limited but effective number of inorganic predictors of origin. [ABSTRACT FROM AUTHOR]

Abstract: Wrapper feature selection approaches are widely used to choose a small subset of relevant features from a dataset. However, Wrappers suffer from the fact that they only use a single classifier. The downside to this is that each classifier will have its own biases and will therefore select very different features. To overcome the biases of individual classifiers, we propose a new data mining method called Wrapper-based Decision Trees (WDT). The WDT method uses multiple classifiers for selecting relevant features and decision trees to visualize relationships among the selected features. We use the WDT to investigate the influences of the levels of computer experience on users’ preferences for the design of search engines. The benefit of using WDT lies within the fact that it can uncover the most accurate set of relevant features to help differentiate the preferences of users with diverse levels of computer experience. The results indicate that the users with varied levels of computer experiences have different preferences regarding the following features: the number of icons, the arrangement of search results, and the presentation of error messages. Such findings can be used to develop personalized search engines to accommodate users’ different levels of computer experience. [ABSTRACT FROM AUTHOR]

Data mining methods are widely used across many disciplines to identify patterns, rules, or associations among huge volumes of data. Data mining methods with explanation capability such as decision tree induction are preferred in many domains. The aim of this paper is to discuss how to deal with the result of decision tree induction methods. This paper has been prompted by the fact that domain experts are able to use the tools for decision tree induction but have great difficulties in interpreting the results. When the domain expert has learnt two decision trees that are from the same domain but based on different data sets as a result of further data collection, he is faced with the problem of how to interpret the different trees. The comparison of two decision trees is therefore an important issue as the user needs such a comparison in order to understand what has changed. We have proposed to provide him with a measure of correspondence between the two trees that allows him to judge if he can accept the changes or not. In this paper, we propose a proper similarity measure. In case of a low similarity value, the expert has evidence to start exploring the reason for this change. Often, he can find things in the data acquisition that might have resulted in some noise and might be fixed. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Abstract: In real world situations, explanations for the same observations may be different depending on perceptions or contexts. They may change with time especially when concept drift occurs. This phenomenon incurs ambiguities. It is useful if an algorithm can learn to reflect ambiguities and select the best decision according to context or situation. Based on this viewpoint, we study the problem of deriving ambiguous decision trees from data streams to cope with concept drift. CVFDT (Concept-adapting Very Fast Decision Tree) is one of the most well-known streaming data mining methods that can learn decision trees incrementally. In this paper, we establish a method called ambiguous CVFDT (aCVFDT), which integrates ambiguities into CVFDT by exploring multiple options at each node whenever a node is to be split. When aCVFDT is used to make class predictions, it is guaranteed that the best and newest knowledge is used. When old concepts recur, aCVFDT can immediately relearn them by using the corresponding options recorded at each node. Furthermore, CVFDT does not automatically detect occurrences of concept drift and only scans trees periodically, whereas an automatic concept drift detecting mechanism is used in aCVFDT. In our experiments, hyperplane problem and two benchmark problems from the UCI KDD Archive, namely Network Intrusion and Forest CoverType, are used to validate the performance of aCVFDT. The experimental results show that aCVFDT obtains significantly improved results over traditional CVFDT. [Copyright &y& Elsevier]

A lottery offers rare opportunities for a favorable wager with a positive expected ticket value. We test various data mining methods for predicting when such opportunities may arise under suitable jackpot conditions. Two models – a linear and logistic regression – are shown to make excellent predictions for favorability of a wager on the next lottery drawing. A simpler classification decision tree provides good predictions as well. These models are practical for a bettor in that they utilize easily estimated ticket sales for the previous rolled-over jackpot and the size of that cash jackpot prize. • Mega Millions and Powerball lotteries sometimes offer a fair jackpot wager. • Predict a fair wager based on jackpot size and number of prizes in last drawing. • Logistic regression and classification decision tree predict a favorable jackpot. [ABSTRACT FROM AUTHOR]

Univariate decision tree algorithms are widely used in data mining because (i) they are easy to learn (ii) when trained they can be expressed in rule based manner. In several applications mainly including data mining, the dataset to be learned is very large. In those cases it is highly desirable to construct univariate decision trees in reasonable time. This may be accomplished by parallelizing univariate decision tree algorithms. In this paper, we first present two different univariate decision tree algorithms C4.5 and univariate linear discriminant tree. We show how to parallelize these algorithms in three ways: (i) feature based; (ii) node based; (iii) data based manners. Experimental results show that performance of the parallelizations highly depend on the dataset and the node based parallelization demonstrate good speedups. [Copyright &y& Elsevier]

This paper presents practical aspects of feature set decomposition in classification problems using decision trees. Feature set decomposition generalizes the task of feature selection which is extensively used in data mining. Feature selection aims to provide a representative set of features from which a classifier is constructed. On the other hand, feature set decomposition decomposes the original set of features into several subsets, and builds a classifier for each subset. The classifiers are then combined for classifying new instances. In order to examine the idea, a general framework that searches for helpful decomposition structures is proposed. This framework nests many algorithms, two of which are tested empirically over a set of benchmark datasets. The first algorithm performs a serial search while using a new Vapnik-Chervonenkis dimension bound for multiple oblivious trees as an evaluating schema. The second algorithm performs a multi-search while using wrapper evaluating schema. This work indicates that feature set decomposition can increase the accuracy of decision trees. [ABSTRACT FROM AUTHOR]

This paper presents a novel Fourier analysis-based approach to combine, transmit, and visualize decision trees in a mobile environment. Fourier representation of a decision tree has several interesting properties that are particularly useful for mining data streams from small mobile computing devices connected through limited-bandwidth wireless networks. This paper presents algorithms to compute the Fourier spectrum of a decision tree and outlines a technique to construct a decision tree from its Fourier spectrum. It offers a framework to aggregate decision trees in their Fourier representations. It also describes the MobiMine, a mobile data stream mining system, that uses the developed techniques for mining stock-market data from handheld devices. [ABSTRACT FROM AUTHOR]

Alternative methods are available for a wide range of medical conditions. Idealistically, doctors would have a tool that would analyse their patients' symptoms and suggest the most accurate diagnosis and treatment plan. Artificial intelligence uses decision trees to predict and classify large datasets. A decision tree is a versatile prediction model. Its main purpose is to learn from observations and logic. Rule-based prediction systems represent and categorize events. We discuss the basic properties of decision trees and successful medical alternatives to the classic induction strategy. The study reviews some of the most important medical applications of decision trees (classification). We show researchers and managers how to accurately assess hospital and epidemic management behaviour. Additionally, we discuss decision trees and their applications. The results showed the effectiveness of decision trees in processing medical data by using internet of things (IoT) and artificial intelligence technologies in medical applications. Accordingly, the researchers recommend the use of these technologies in other fields of studies. [ABSTRACT FROM AUTHOR]

It is of great significance to scientifically analyze and excavate the internal relationship between traffic accident data, find out the potential laws hidden in the different attributes of the accident data, and thus provide suggestions and theoretical and technical support for the management decisions of relevant departments. In order to overcome the low recall rate, low precision rate and long task completion time in traditional traffic accident data collaborative mining methods, a traffic accident data collaborative mining method using decision tree and association rules is proposed. First of all, use the Map Reduce computing framework to build a traffic accident data collaborative mining framework; Then, combining with the weighted model to improve the association rules, the traffic accident data is preliminarily mined; Finally, the attribute with the largest information gain is calculated, and the traffic accident data is classified using the decision tree to realize the collaborative mining of traffic accident data. The experimental results show that the recall and precision of this method can reach 98.75% and 98.74% respectively, and the mining task completion time is only 0.2s at the fastest, which has good application effect. [ABSTRACT FROM AUTHOR]

Tropical cyclone (TC) intensity prediction, especially in the warning time frame of 24-48 h and for the prediction of rapid intensification (RI), remains a major operational challenge. Sea surface temperature (SST) based empirical or theoretical maximum potential intensity (MPI) is the most important predictor in statistical intensity prediction schemes and rules derived by data mining techniques. Since the underlying SSTs during TCs usually cannot be observed well by satellites because of rain contamination and cannot be produced on a timely basis for operational statistical prediction, an ocean coupling potential intensity index (OC_PI), which is calculated based on pre-TC averaged ocean temperatures from the surface down to 100 m, is demonstrated to be important in building the decision tree for the classification of 24-h TC intensity change Δ V24, that is, RI (Δ V24 ≥ 25 kt, where 1 kt = 0.51 m s−1) and non-RI (Δ V24 < 25 kt). Cross validations using 2000-10 data and independent verification using 2011 data are performed. The decision tree with the OC_PI shows a cross-validation accuracy of 83.5% and an independent verification accuracy of 89.6%, which outperforms the decision tree excluding the OC_PI with corresponding accuracies of 83.2% and 83.9%. Specifically for RI classification in independent verification, the former decision tree shows a much higher probability of detection and a lower false alarm ratio than the latter example. This study is of great significance for operational TC RI prediction as pre-TC OC_PI can skillfully reduce the overestimation of storm potential intensity by traditional SST-based MPI, especially for the non-RI TCs. [ABSTRACT FROM AUTHOR]

Structural equation model trees (SEM Trees) allow for the construction of decision trees with structural equation models fit in each of the nodes. Based on covariate information, SEM Trees can be used to create distinct subgroups containing individuals with similar parameter estimates. Currently, the structural equation modeling component of SEM Trees is implemented in the R packages OpenMx and lavaan. We extend SEM Trees so that the models can be fit in Mplus, in the hopes that its efficiency and accessibility allow a broader group of researchers to fit a wider range of models. We discuss the Mplus Trees algorithm, its implementation, and its position among the growing number of tree-based methods in psychological research. We also provide several examples using publicly available data to illustrate how Mplus Trees can be implemented in practice with the R package MplusTrees. [ABSTRACT FROM AUTHOR]

• In this paper, a methodology to speed up fuel lattices optimization is shown. • A recurrent neural network to explore the solutions space was used. • Two new objective functions were studied. • Decision trees to estimate variables involved in objective functions were constructed. • This methodology was tested obtaining a good NN-DT performance. In this paper a new methodology to speed up the fuel lattice design optimization in a BWR is explored. In previous works, fuel lattice optimization was made using LPPF (Local Power Peaking Factor) at the beginning of the fuel lattice life. However, undesirable LPPF vs. fuel lattice exposure behaviors were observed. Due to this, LPPF vs. fuel lattice exposure was calculated through out fuel lattice life burnup. From a computational point of view, such calculation is very expensive when done using the CASMO-4 code. A new methodology to speed up the optimization was proposed based on two aspects: in one side, using objective functions that take into account LPPF vs. fuel lattice exposure and residual gadolinia; and in other side, using decision trees to estimate some fuel lattice parameters in a fast and reliable way. It could be verified that decision trees estimations had the enough reliability to be used into an optimization process to discard bad fuel lattice configurations and speed up the optimization process. At the end of this process, CASMO-4 code is used to calculate the final fuel lattice parameters. In this way, fuel lattice optimization time was reduced from 6 hours to 15 minutes obtaining good LPPF vs exposure behaviors. [ABSTRACT FROM AUTHOR]