Incremental updating approximations for double-quantitative decision-theoretic rough sets with the variation of objects

Guo, Yanting; Tsang, Eric W. K.; Hu, Meng; Lin, Xuxin; Chen, Degang; Xu, Weihua; Sang, Binbin

doi:10.1016/j.knosys.2019.105082

Cited by 30 publications

(6 citation statements)

References 44 publications

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“…Some researchers have extended Pawlak's idea by incorporating fuzzy equivalence relations, neighborhood relations and dominance relations into Pawlak rough sets to form neighborhood rough sets [3,4], fuzzy rough sets [5][6][7][8][9], and dominance-based rough sets [10][11][12]. The generalized models of rough set are commonly applied in the reduction of attributes [13][14][15], feature selection [16][17][18][19], extraction of rules [20][21][22][23], theory of decisions [24][25][26], incremental learning [27][28][29],…”

Section: Introductionmentioning

confidence: 99%

Attribute Reduction for Information Systems via Strength of Rules and Similarity Matrix

Eid¹,

Medhat²,

Ali³

2023

Computer Systems Science and Engineering

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An information system is a type of knowledge representation, and attribute reduction is crucial in big data, machine learning, data mining, and intelligent systems. There are several ways for solving attribute reduction problems, but they all require a common categorization. The selection of features in most scientific studies is a challenge for the researcher. When working with huge datasets, selecting all available attributes is not an option because it frequently complicates the study and decreases performance. On the other side, neglecting some attributes might jeopardize data accuracy. In this case, rough set theory provides a useful approach for identifying superfluous attributes that may be ignored without sacrificing any significant information; nonetheless, investigating all available combinations of attributes will result in some problems. Furthermore, because attribute reduction is primarily a mathematical issue, technical progress in reduction is dependent on the advancement of mathematical models. Because the focus of this study is on the mathematical side of attribute reduction, we propose some methods to make a reduction for information systems according to classical rough set theory, the strength of rules and similarity matrix, we applied our proposed methods to several examples and calculate the reduction for each case. These methods expand the options of attribute reductions for researchers.

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Section: Introductionmentioning

confidence: 99%

Attribute Reduction for Information Systems via Strength of Rules and Similarity Matrix

Eid¹,

Medhat²,

Ali³

2023

Computer Systems Science and Engineering

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“…However, the data always change over time in reality. In this regard, some scholars have done a lot of researches mainly from three viewpoints: attribute change [26]- [28], attribute value change [29]- [31], and object change [32]- [34]. In the information system over two universes, Hu et al [35] proposed a kind of incremental algorithm of fuzzy probabilistic rough sets.…”

Section: Introductionmentioning

confidence: 99%

Object-First Incremental Algorithms for Updating Approximations in Multi-Granulation Fuzzy Probabilistic Rough Sets Over Two Universes

et al. 2021

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“…There are many classical feature selection approaches, such as Lasso (Tibshirani 1996), Ridge regression (Hoerl and Kennard 1970a, b), minimal-redundancy-maximal-relevance (Peng et al 2005), Relief-F (Robnik-Sikonja and Kononenko 2003), fuzzy rough sets (Tsang et al 2008;Yang et al 2018;Ni et al 2020), neighborhood rough sets (Hu et al 2008c), evolutionary computing (Tawhid and Ibrahim 2020;Mafarja and Mirjalili 2018) and others (Hu et al 2020(Hu et al , 2021aWang et al 2020a;Hu et al 2018). These feature selection approaches are used in decision theory (Guo et al 2020a), gene selection (Maji and Paul 2011), cancer classification (Arunkumar and Ramakrishnan 2018), text clustering (Abualigah et al 2021), classification (Zhao et al 2010;Wang et al 2018a) and other fields (Guo et al 2019(Guo et al , 2020bWang et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Feature selection based on min-redundancy and max-consistency

Guo

Tsang

et al. 2021

Adv. in Comp. Int.

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Feature selection can effectively eliminate irrelevant or redundant features without changing features semantics, so as to improve the performance of learning and reduce the training time. In most of the existing feature selection methods based on rough sets, eliminating the redundant features between features and decisions, and deleting the redundant features between features are performed separately. This will greatly increase the search time of feature subset. To quickly remove redundant features, we define a series of feature evaluation functions that consider both the consistency between features and decisions, and redundancy between features, then propose a novel feature selection method based on min-redundancy and max-consistency. Firstly, we define the consistency of features with respect to decisions and the redundancy between features from neighborhood information granules. Then we propose a combined criterion to measure the importance of features and design a feature selection algorithm based on minimal-redundancy-maximal-consistency (mRMC). Finally, on UCI data sets, mRMC is compared with three other popular feature selection algorithms based on neighborhood idea, from classification accuracy, the number of selected features and running time. The experimental comparison shows that mRMC can quickly delete redundant features and select useful features while ensuring classification accuracy.

show abstract

Incremental updating approximations for double-quantitative decision-theoretic rough sets with the variation of objects

Cited by 30 publications

References 44 publications

Attribute Reduction for Information Systems via Strength of Rules and Similarity Matrix

Attribute Reduction for Information Systems via Strength of Rules and Similarity Matrix

Object-First Incremental Algorithms for Updating Approximations in Multi-Granulation Fuzzy Probabilistic Rough Sets Over Two Universes

Feature selection based on min-redundancy and max-consistency

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