文献类型：期刊文献

英文题名：Classification rule discovery with DE/QDE algorithm

作者：Su, Haijun[1];Yang, Yupu[1];Zhao, Liang[2]

机构：[1]Shanghai Jiao Tong Univ, Dept Automat, Shanghai 200030, Peoples R China;[2]E China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China

年份：2010

卷号：37

期号：2

起止页码：1216

外文期刊名：EXPERT SYSTEMS WITH APPLICATIONS

收录：;EI(收录号：20095112558427);WOS:【SCI-EXPANDED(收录号:WOS:000272432300038)】；

语种：英文

外文关键词：Classification; Quantum-inspired; Differential evolution; Data mining; Continuous attribute

摘要：The quantum-inspired differential evolution algorithm (QDE) is a new optimization algorithm in the binary-valued space. The paper proposes the DE/QDE algorithm for the discovery of classification rules DE/QDE combines the characteristics of the conventional DE algorithm and the QDE algorithm. Based on some strategies of DE and QDE. DE/QDE can directly cope with the continuous, nominal attributes without discretizing the continuous attributes in the preprocessing step. DE/QDE also has specific weight mutation for managing the weight value of the individual encoding. Then DE/QDE is compared with Ant-Miner and CN2 on six problems from the LICI repository datasets. The results indicate that DE/QDE is competitive with Ant-Miner and CN2 in term of the predictive accuracy. (C) 2009 Elsevier Ltd All rights reserved.