Fuzzy-rough feature selection accelerator

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

103 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)61-78
Journal / PublicationFuzzy Sets and Systems
Volume258
Online published22 May 2014
Publication statusPublished - 1 Jan 2015

Abstract

Fuzzy rough set method provides an effective approach to data mining and knowledge discovery from hybrid data including categorical values and numerical values. However, its time-consumption is very intolerable to analyze data sets with large scale and high dimensionality. Many heuristic fuzzy-rough feature selection algorithms have been developed however, quite often, these methods are still computationally time-consuming. For further improvement, we propose an accelerator, called forward approximation, which combines sample reduction and dimensionality reduction together. The strategy can be used to accelerate a heuristic process of fuzzy-rough feature selection. Based on the proposed accelerator, an improved algorithm is designed. Through the use of the accelerator, three representative heuristic fuzzy-rough feature selection algorithms have been enhanced. Experiments show that these modified algorithms are much faster than their original counterparts. It is worth noting that the performance of the modified algorithms becomes more visible when dealing with larger data sets.

Research Area(s)

  • Accelerator, Feature selection, Forward approximation, Fuzzy rough sets, Granular computing, Rough sets

Citation Format(s)

Fuzzy-rough feature selection accelerator. / Qian, Yuhua; Wang, Qi; Cheng, Honghong; Liang, Jiye; Dang, Chuangyin.

In: Fuzzy Sets and Systems, Vol. 258, 01.01.2015, p. 61-78.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review