Knowledge reduction based on divide and conquer method in rough set theory
type: Item
instance of: scholarly article
title: Knowledge reduction based on divide and conquer method in rough set theory
zbMATH Open document ID: 1264.68175
DOI: 10.1155/2012/864652
author: Feng Hu
author: Guoyin Wang
published in: Mathematical Problems in Engineering
publication date: 2013-06-11T00:00:00Z
review text: Summary: The divide and conquer method is a typical granular computing method using multiple levels of abstraction and granulations. So far, although some achievements based on divided and conquer method in the rough set theory have been acquired, the systematic methods for knowledge reduction based on divide and conquer method are still absent. In this paper, the knowledge reduction approaches based on divide and conquer method, under equivalence relation and under tolerance relation, are presented, respectively. After that, a systematic approach, named as the abstract process for knowledge reduction based on divide and conquer method in rough set theory, is proposed. Based on the presented approach, two algorithms for knowledge reduction, including an algorithm for attribute reduction and an algorithm for attribute value reduction, are presented. Some experimental evaluations are done to test the methods on uci data sets and KDDCUP99 data sets. The experimental results illustrate that the proposed approaches are efficient to process large data sets with good recognition rate, compared with KNN, SVM, C4.5, Naive Bayes, and CART.
Mathematics Subject Classification ID: 68T37
Mathematics Subject Classification ID: 91B44
zbMATH DE Number: 6173651
Wikidata QID: Q58912303
describes a project that uses: C4.5
describes a project that uses: LERS
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2012/864652
OpenAlex ID: W2093938028
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description: scientific article; zbMATH DE number 6173651