Incremental Attribute Reduction Based on Elementary Sets

Hu, Feng; Wang, Guoyin; Huang, Hai; Wu, Yu

doi:10.1007/11548669_20

Cited by 71 publications

(37 citation statements)

References 7 publications

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“…Similar to Ref. [15], for five datasets, the values of the former nine condition features are generated randomly from 0 to 1, the values of the latter remaining condition features and decision feature are generated randomly from 0 to 9. Briefly, the synthetic datasets are described as follows: (7) The experimental results are shown in Table 2, where T is running time of algorithm, its unit is second and only keeps one decimal digits.…”

Section: Resultsmentioning

confidence: 99%

A novel approach to improving C-Tree for feature selection

Yang¹,

Yang²

2011

Applied Soft Computing

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Section: Resultsmentioning

confidence: 99%

A novel approach to improving C-Tree for feature selection

Yang¹,

Yang²

2011

Applied Soft Computing

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“…In [7,8,20], authors used positive region and discernibility matrix for reduction algorithms when adding new objects. W. Qian and other authors proposed an incremental algorithm for feature reduction in decision tables using dependency function in the case of adding or deleting a feature subset (see [16]).…”

Section: Introductionmentioning

confidence: 99%

Metric Based Attribute Reduction Method in Dynamic Decision Tables

Demetrovics

Hương²,

Thi³

et al. 2016

Cybernetics and Information Technologies

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“…Some novel extended models [9]- [11] and many rough setbased attribute reduction algorithms [12]- [16] have been introduced to date. Generally, the rough setbased attribute reduction methods can roughly be divided into the following categories: discernibility matrix-based methods [12]- [14],positive regionbased methods [15], heuristic strategy-based methods [8] and other evolutionary methods [16]. It is well known that the main objective of rough setbased attribute reduction methods is to provide those attribute subsets with the best or top n quality for post-analysis algorithms.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the quality of attribute reductions obtained using rough set by margin criteria

Yang¹,

Qiu²,

Ji³

2007

Proceedings on Intelligent Systems and Knowledge Engineering (ISKE2007)

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As a filter model, rough set-based methods are one of effective attribute reduction(also called feature selection) that preserve the meaning of the features. In rough set theory, researchers mainly focus on extension of the classical rough set model(also called Pawlak model for short) and development of efficient attribute reduction algorithms. However, very little work has been done for aiming on the evaluation of the quality of attribute reduction, except that employing the cardinality of the given attribute subset P and so-called approximation quality of P or other equivalent criteria induced by Pawlak model. Although this discrimination strategy is simple and effective in most cases, it is very difficulty to guarantee the selected attribute reduction(s) from lots of attribute reductions are the best or Top n, especially for the case containing many attribute reductions with same cardinality and approximation quality. Therefore, in this paper, we incorporate margin criteria into the proposed evaluation mechanism for guaranteeing the effectiveness of the selected attribute subsets, since margin, originally designed for binary classification problem using support vector machine, can actually determine the generalization ability. Also, an improved discernibility function-based algorithm is proposed. To further test the effectiveness of the proposed method, the algorithm of this paper is experimented using UCI benchmark datasets. Preliminary experimental results show that the attribute reductions with larger margin have better or comparable performance than those with relatively small margin for all reducts with same cardinality. Thus, our newly developed method can, in most cases, get more effective attribute subsets.

show abstract

Incremental Attribute Reduction Based on Elementary Sets

Cited by 71 publications

References 7 publications

A novel approach to improving C-Tree for feature selection

A novel approach to improving C-Tree for feature selection

Metric Based Attribute Reduction Method in Dynamic Decision Tables

Evaluation of the quality of attribute reductions obtained using rough set by margin criteria

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