An incremental algorithm for attribute reduction with variable precision rough sets

Chen, Degang; Yang, Yong; Dong, Ze

doi:10.1016/j.asoc.2016.04.003

Cited by 86 publications

(13 citation statements)

References 17 publications

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“…At the arrival of a new sample, Yang [50] proposed an incremental feature selection algorithm by updating the discernibility matrix. At the arrival of a sample, Chen et al [51] proposed an incremental algorithm for feature selection based on variable precision rough sets by the strategies of adding and deleting features. As a sample with real-valued features arrives, Li et al [60] investigated the incremental mechanisms for feature selection based on neighorhood rough sets.…”

Section: Rough Set Based Incremental Feature Selectionmentioning

confidence: 99%

Incremental Perspective for Feature Selection Based on Fuzzy Rough Sets

Yang

Chen

Wang

et al. 2018

IEEE Trans. Fuzzy Syst.

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107

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Feature selection based on fuzzy rough sets is an effective approach to select a compact feature subset that optimally predicts a given decision label. Despite being studied extensively, most existing methods of fuzzy rough set based feature selection are restricted to computing the whole data set in batch, which is often costly or even intractable for large data sets. To improve the time efficiency, we investigate the incremental perspective for fuzzy rough set based feature selection assuming data can be presented in sample subsets one after another. The key challenge for the incremental perspective is how to add and delete features with the subsequent arrival of sample subsets. We tackle this challenge with strategies of adding and deleting features based on the relative discernibility relations that are updated as subsets arrive sequentially. Two incremental algorithms for fuzzy rough set based feature selection are designed based on the strategies. One updates the selected features as each sample subset arrives, and outputs the final feature subset where no sample subset is left. The other updates the relative discernibility relations but only performs feature selection where there is no further subset arriving. Experimental comparisons suggest our incremental algorithms expedite fuzzy rough set based feature selection without compromising performance.

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Section: Rough Set Based Incremental Feature Selectionmentioning

confidence: 99%

Incremental Perspective for Feature Selection Based on Fuzzy Rough Sets

Yang

Chen

Wang

et al. 2018

IEEE Trans. Fuzzy Syst.

Self Cite

107

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“…The classification precision can be controlled by a variable factor of the positive region. The variable precision RST has been widely used in many fields such as decision analysis [37], process control [38], data clustering [39] and attribute selection [40].…”

Section: Introductionmentioning

confidence: 99%

Feature Subset Selection Based on Variable Precision Neighborhood Rough Sets

Chen¹,

Chen²

2021

IJCIS

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Rough sets have been widely used in the fields of machine learning and feature selection. However, the classical rough sets have the problems of difficultly dealing with real-value data and weakly fault tolerance. In this paper, by introducing a neighborhood rough set model, the values of decision systems are granulated into some condition and decision neighborhood granules. A concept of neighborhood granular swarm is defined in a decision system. Then the sizes of a neighborhood granule and a neighborhood granular swarm are also given. In order to enhance the fault-tolerant ability of classification systems, we define some concepts of granule inclusion, variable precision neighborhood approximation sets and positive region. We propose a variable precision neighborhood rough set model, and analyze its property. Furthermore, based on the positive region of a variable precision neighborhood, we give the significance of an attribute and use it to select feature subsets. A feature subset selection algorithm to the variable precision neighborhood rough sets is designed. Finally, the feature selection algorithm is carried out on the UCI datasets, and the selected features are tested by the support vector machine (SVM) classification algorithm. Theoretical analysis and experiments show that the proposed method can find the effective and compact feature subsets, which have abilities of fault tolerance.

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“…To overcome this disadvantage, a new attribute reduction approach was proposed, namely, the method of incremental attribute reduction [15][16][17][18][19][20] . The main principle of the proposed algorithm is the utilization of the reduction results for original information systems to incrementally compute the reduction for the new information system, which reduces the amount of unnecessary recalculations and greatly improves the reduction efficiency [15,17,20] . The dynamic variation of the information system has multiple forms, and one of the common forms is the attributes set with gradually increasing.…”

Section: Introductionmentioning

confidence: 99%

Valid Incremental Attribute Reduction Algorithm Based on Attribute Generalization for an Incomplete Information System

Guang-qiong

2019

Chinese Journal of Electronics

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An incremental algorithm for attribute reduction with variable precision rough sets

Cited by 86 publications

References 17 publications

Incremental Perspective for Feature Selection Based on Fuzzy Rough Sets

Incremental Perspective for Feature Selection Based on Fuzzy Rough Sets

Feature Subset Selection Based on Variable Precision Neighborhood Rough Sets

Valid Incremental Attribute Reduction Algorithm Based on Attribute Generalization for an Incomplete Information System

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