The Experts below are selected from a list of 123 Experts worldwide ranked by ideXlab platform
Duoqian Miao - One of the best experts on this subject based on the ideXlab platform.
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an expanded double Quantitative model regarding probabilities and grades and its hierarchical double Quantitative Attribute reduction
Information Sciences, 2015Co-Authors: Xianyong Zhang, Duoqian MiaoAbstract:A double-Quantitative model on probabilities and grades is proposed by integration.The model with Quantitative semantics expands DTRS-Model, GRS-Model, Pawlak-Model.Model regions are hierarchically and optimally calculated via two algorithms.Three reduct types - SRP-Reduct, CRP-Reduct, APP-Reduct - are hierarchically studied.The double-Quantitative reducts expand basic qualitative and Quantitative reducts. Probabilities and grades serve as relative and absolute measures, respectively. They are used to establish the decision-theoretic rough set (DTRS) and graded rough set (GRS) - two basic Quantitative models. The double-quantification of probabilities and grades exhibits systematicness and completeness in view of the two-dimensional feature of the approximate space; however, double-Quantitative construction becomes a problem, and double-Quantitative reduction is rarely reported. Thus, this paper mainly constructs an expanded double-Quantitative model by logically integrating probabilities and grades; it further studies relevant double-Quantitative reduction by hierarchically preserving specific regions. (1) First, a novel model is established via the logic integration and expansion requirement, and its regional system and granular hierarchy are studied via granular computing. Thus, regional semantics is extracted via basic semantics granules. Regional calculation is realized by two algorithms, and the algorithm regarding calculation granules exhibits optimization according to algorithm analyses. (2) Second, three types of model-regional preservation reducts and their hierarchy are discussed in the two-category case. Thus, SRP-Reduct, CRP-Reduct, and APP-Reduct are studied by exploring four-region preservation properties, constructing two-region classification regions, and preserving four original approximations, respectively. Furthermore, a relevant reduction hierarchy is thoroughly achieved. (3) Moreover, the model and its reduction are illustrated by two examples of decision tables. The constructional model conducts double-quantification regarding probabilities and grades; thus, it exhibits double-Quantitative semantics and benignly expands DTRS-Model, GRS-Model, and Pawlak-Model. Furthermore, its hierarchical reduction reflects some double-Quantitative reduction essence; thus, its reduction expands qualitative Pawlak-Reduction while guides Quantitative DTRS-Reduction and GRS-Reduction.
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region based Quantitative and hierarchical Attribute reduction in the two category decision theoretic rough set model
Knowledge Based Systems, 2014Co-Authors: Xianyong Zhang, Duoqian MiaoAbstract:Quantitative Attribute reduction exhibits applicability but complexity when compared to qualitative reduction. According to the two-category decision theoretic rough set model, this paper mainly investigates Quantitative reducts and their hierarchies (with qualitative reducts) from a regional perspective. (1) An improved type of classification regions is proposed, and its preservation reduct (CRP-Reduct) is studied. (2) Reduction targets and preservation properties of set regions are analyzed, and the set-region preservation reduct (SRP-Reduct) is studied. (3) Separability of set regions and rule consistency is verified, and the Quantitative and qualitative double-preservation reduct (DP-Reduct) is established. (4) Hierarchies of CRP-Reduct, SRP-Reduct, and DP-Reduct are explored with two qualitative reducts: the Pawlak-Reduct and knowledge-preservation reduct (KP-Reduct). (5) Finally, verification experiments are provided. CRP-Reduct, SRP-Reduct, and DP-Reduct expand layer by layer Pawlak-Reduct and exhibit Quantitative applicability, and the experimental results indicate their effectiveness and hierarchies regarding Pawlak-Reduct and KP-Reduct.
Xianyong Zhang - One of the best experts on this subject based on the ideXlab platform.
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an expanded double Quantitative model regarding probabilities and grades and its hierarchical double Quantitative Attribute reduction
Information Sciences, 2015Co-Authors: Xianyong Zhang, Duoqian MiaoAbstract:A double-Quantitative model on probabilities and grades is proposed by integration.The model with Quantitative semantics expands DTRS-Model, GRS-Model, Pawlak-Model.Model regions are hierarchically and optimally calculated via two algorithms.Three reduct types - SRP-Reduct, CRP-Reduct, APP-Reduct - are hierarchically studied.The double-Quantitative reducts expand basic qualitative and Quantitative reducts. Probabilities and grades serve as relative and absolute measures, respectively. They are used to establish the decision-theoretic rough set (DTRS) and graded rough set (GRS) - two basic Quantitative models. The double-quantification of probabilities and grades exhibits systematicness and completeness in view of the two-dimensional feature of the approximate space; however, double-Quantitative construction becomes a problem, and double-Quantitative reduction is rarely reported. Thus, this paper mainly constructs an expanded double-Quantitative model by logically integrating probabilities and grades; it further studies relevant double-Quantitative reduction by hierarchically preserving specific regions. (1) First, a novel model is established via the logic integration and expansion requirement, and its regional system and granular hierarchy are studied via granular computing. Thus, regional semantics is extracted via basic semantics granules. Regional calculation is realized by two algorithms, and the algorithm regarding calculation granules exhibits optimization according to algorithm analyses. (2) Second, three types of model-regional preservation reducts and their hierarchy are discussed in the two-category case. Thus, SRP-Reduct, CRP-Reduct, and APP-Reduct are studied by exploring four-region preservation properties, constructing two-region classification regions, and preserving four original approximations, respectively. Furthermore, a relevant reduction hierarchy is thoroughly achieved. (3) Moreover, the model and its reduction are illustrated by two examples of decision tables. The constructional model conducts double-quantification regarding probabilities and grades; thus, it exhibits double-Quantitative semantics and benignly expands DTRS-Model, GRS-Model, and Pawlak-Model. Furthermore, its hierarchical reduction reflects some double-Quantitative reduction essence; thus, its reduction expands qualitative Pawlak-Reduction while guides Quantitative DTRS-Reduction and GRS-Reduction.
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region based Quantitative and hierarchical Attribute reduction in the two category decision theoretic rough set model
Knowledge Based Systems, 2014Co-Authors: Xianyong Zhang, Duoqian MiaoAbstract:Quantitative Attribute reduction exhibits applicability but complexity when compared to qualitative reduction. According to the two-category decision theoretic rough set model, this paper mainly investigates Quantitative reducts and their hierarchies (with qualitative reducts) from a regional perspective. (1) An improved type of classification regions is proposed, and its preservation reduct (CRP-Reduct) is studied. (2) Reduction targets and preservation properties of set regions are analyzed, and the set-region preservation reduct (SRP-Reduct) is studied. (3) Separability of set regions and rule consistency is verified, and the Quantitative and qualitative double-preservation reduct (DP-Reduct) is established. (4) Hierarchies of CRP-Reduct, SRP-Reduct, and DP-Reduct are explored with two qualitative reducts: the Pawlak-Reduct and knowledge-preservation reduct (KP-Reduct). (5) Finally, verification experiments are provided. CRP-Reduct, SRP-Reduct, and DP-Reduct expand layer by layer Pawlak-Reduct and exhibit Quantitative applicability, and the experimental results indicate their effectiveness and hierarchies regarding Pawlak-Reduct and KP-Reduct.
Reda Alhajj - One of the best experts on this subject based on the ideXlab platform.
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Multi-objective genetic algorithms based automated clustering for fuzzy association rules mining
Journal of Intelligent Information Systems, 2008Co-Authors: Reda Alhajj, Mehmet KayaAbstract:Researchers realized the importance of integrating fuzziness into association rules mining in databases with binary and Quantitative Attributes. However, most of the earlier algorithms proposed for fuzzy association rules mining either assume that fuzzy sets are given or employ a clustering algorithm, like CURE, to decide on fuzzy sets; for both cases the number of fuzzy sets is pre-specified. In this paper, we propose an automated method to decide on the number of fuzzy sets and for the autonomous mining of both fuzzy sets and fuzzy association rules. We achieve this by developing an automated clustering method based on multi-objective Genetic Algorithms (GA); the aim of the proposed approach is to automatically cluster values of a Quantitative Attribute in order to obtain large number of large itemsets in less time. We compare the proposed multi-objective GA based approach with two other approaches, namely: 1) CURE-based approach, which is known as one of the most efficient clustering algorithms; 2) Chien et al. clustering approach, which is an automatic interval partition method based on variation of density. Experimental results on 100 K transactions extracted from the adult data of USA census in year 2000 showed that the proposed automated clustering method exhibits good performance over both CURE-based approach and Chien et al.’s work in terms of runtime, number of large itemsets and number of association rules.
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Utilizing Genetic Algorithms to Optimize Membership Functions for Fuzzy Weighted Association Rules Mining
Applied Intelligence, 2006Co-Authors: Mehmet Kaya, Reda AlhajjAbstract:It is not an easy task to know a priori the most appropriate fuzzy sets that cover the domains of Quantitative Attributes for fuzzy association rules mining. In general, it is unrealistic that experts can always provide such sets. And finding the most appropriate fuzzy sets becomes a more complex problem when items are not considered to have equal importance and the support and confidence parameters required for the association rules mining process are specified as linguistic terms. Existing clustering based automated methods are not satisfactory because they do not consider the optimization of the discovered membership functions. In order to tackle this problem, we propose Genetic Algorithms (GAs) based clustering method, which dynamically adjusts the fuzzy sets to provide maximum profit based on user specified linguistic minimum support and confidence terms. This is achieved by tuning the base values of the membership functions for each Quantitative Attribute with respect to two different evaluation functions maximizing the number of large itemsets and the average of the confidence intervals of the generated rules. To the best of our knowledge, this is the first effort in this direction. Experiments conducted on 100 K transactions from the adult database of United States census in year 2000 demonstrate that the proposed clustering method exhibits good performance in terms of the number of produced large itemsets and interesting association rules.
Mehmet Kaya - One of the best experts on this subject based on the ideXlab platform.
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Multi-objective genetic algorithms based automated clustering for fuzzy association rules mining
Journal of Intelligent Information Systems, 2008Co-Authors: Reda Alhajj, Mehmet KayaAbstract:Researchers realized the importance of integrating fuzziness into association rules mining in databases with binary and Quantitative Attributes. However, most of the earlier algorithms proposed for fuzzy association rules mining either assume that fuzzy sets are given or employ a clustering algorithm, like CURE, to decide on fuzzy sets; for both cases the number of fuzzy sets is pre-specified. In this paper, we propose an automated method to decide on the number of fuzzy sets and for the autonomous mining of both fuzzy sets and fuzzy association rules. We achieve this by developing an automated clustering method based on multi-objective Genetic Algorithms (GA); the aim of the proposed approach is to automatically cluster values of a Quantitative Attribute in order to obtain large number of large itemsets in less time. We compare the proposed multi-objective GA based approach with two other approaches, namely: 1) CURE-based approach, which is known as one of the most efficient clustering algorithms; 2) Chien et al. clustering approach, which is an automatic interval partition method based on variation of density. Experimental results on 100 K transactions extracted from the adult data of USA census in year 2000 showed that the proposed automated clustering method exhibits good performance over both CURE-based approach and Chien et al.’s work in terms of runtime, number of large itemsets and number of association rules.
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Utilizing Genetic Algorithms to Optimize Membership Functions for Fuzzy Weighted Association Rules Mining
Applied Intelligence, 2006Co-Authors: Mehmet Kaya, Reda AlhajjAbstract:It is not an easy task to know a priori the most appropriate fuzzy sets that cover the domains of Quantitative Attributes for fuzzy association rules mining. In general, it is unrealistic that experts can always provide such sets. And finding the most appropriate fuzzy sets becomes a more complex problem when items are not considered to have equal importance and the support and confidence parameters required for the association rules mining process are specified as linguistic terms. Existing clustering based automated methods are not satisfactory because they do not consider the optimization of the discovered membership functions. In order to tackle this problem, we propose Genetic Algorithms (GAs) based clustering method, which dynamically adjusts the fuzzy sets to provide maximum profit based on user specified linguistic minimum support and confidence terms. This is achieved by tuning the base values of the membership functions for each Quantitative Attribute with respect to two different evaluation functions maximizing the number of large itemsets and the average of the confidence intervals of the generated rules. To the best of our knowledge, this is the first effort in this direction. Experiments conducted on 100 K transactions from the adult database of United States census in year 2000 demonstrate that the proposed clustering method exhibits good performance in terms of the number of produced large itemsets and interesting association rules.
Geoffrey I Webb - One of the best experts on this subject based on the ideXlab platform.
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On the Effectiveness of Discretizing Quantitative Attributes in Linear Classifiers
arXiv: Learning, 2017Co-Authors: Nayyar A. Zaidi, Geoffrey I WebbAbstract:Learning algorithms that learn linear models often have high representation bias on real-world problems. In this paper, we show that this representation bias can be greatly reduced by discretization. Discretization is a common procedure in machine learning that is used to convert a Quantitative Attribute into a qualitative one. It is often motivated by the limitation of some learners to qualitative data. Discretization loses information, as fewer distinctions between instances are possible using discretized data relative to undiscretized data. In consequence, where discretization is not essential, it might appear desirable to avoid it. However, it has been shown that discretization often substantially reduces the error of the linear generative Bayesian classifier naive Bayes. This motivates a systematic study of the effectiveness of discretizing Quantitative Attributes for other linear classifiers. In this work, we study the effect of discretization on the performance of linear classifiers optimizing three distinct discriminative objective functions --- logistic regression (optimizing negative log-likelihood), support vector classifiers (optimizing hinge loss) and a zero-hidden layer artificial neural network (optimizing mean-square-error). We show that discretization can greatly increase the accuracy of these linear discriminative learners by reducing their representation bias, especially on big datasets. We substantiate our claims with an empirical study on $42$ benchmark datasets.
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incremental discretization for naive bayes classifier
Lecture Notes in Computer Science, 2006Co-Authors: Jingli Lu, Ying Yang, Geoffrey I WebbAbstract:Naive-Bayes classifiers (NB) support incremental learning. However, the lack of effective incremental discretization methods has been hindering NB's incremental learning in face of Quantitative data. This problem is further compounded by the fact that Quantitative data are everywhere, from temperature readings to share prices. In this paper, we present a novel incremental discretization method for NB, incremental flexible frequency discretization (IFFD). IFFD discretizes values of a Quantitative Attribute into a sequence of intervals of flexible sizes. It allows online insertion and splitting operation on intervals. Theoretical analysis and experimental test are conducted to compare IFFD with alternative methods. Empirical evidence suggests that IFFD is efficient and effective. NB coupled with IFFD achieves a rapport between high learning efficiency and high classification accuracy in the context of incremental learning.
