Classification rule discovery with DE/QDE algorithm

被引：17

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

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2010年 / 37卷 / 02期

关键词：

Classification; Quantum-inspired; Differential evolution; Data mining; Continuous attribute;

D O I：

10.1016/j.eswa.2009.06.029

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

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.

引用

页码：1216 / 1222

页数：7

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