A novel chemistry based metaheuristic optimization method for mining of classification rules

被引：60

作者：

Alatas, Bilal ^{[1
]}

机构：

[1] Tunceli Univ, Dept Comp Engn, TR-62000 Tunceli, Turkey

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2012年 / 39卷 / 12期

关键词：

Artificial chemical reaction optimization algorithm; Data mining; Classification rules; Heuristic optimization; Performance; Uniform population; PARTICLE SWARM OPTIMIZATION; CENTRAL FORCE OPTIMIZATION; ARTIFICIAL IMMUNE-SYSTEM; GENETIC ALGORITHM; NEURAL-NETWORKS; SEARCH;

D O I：

10.1016/j.eswa.2012.03.066

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

When investigated carefully, chemical reactions possess efficient objects, states, process, and events that can be designed as a computational method en bloc. In this study, a novel computational method, which is robust and have less parameters than that of used in the literature, is intended to be developed inspiring from types and occurring of chemical reactions. The proposed method is named as artificial chemical reaction optimization algorithm, ACROA. In this study, one of the first applications of this method has been performed in classification rule discovery field of data mining and efficiency has been demonstrated. (C) 2012 Elsevier Ltd. All rights reserved.

引用

页码：11080 / 11088

页数：9

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