Predictive and comprehensible rule discovery using a multi-objective genetic algorithm

被引：48

作者：

Dehuri, S.

Mall, R. ^{[1
]}

机构：

[1] Indian Inst Technol, Dept Comp Sci & Engn, Kharagpur 721302, W Bengal, India

[2] Fakir Mohan Univ, PG Dept Informat & Commun Technol, Vyasa Vihar 756019, Balasore, India

来源：

KNOWLEDGE-BASED SYSTEMS | 2006年 / 19卷 / 06期

关键词：

simple genetic algorithm; Pareto optimal solutions; niched Pareto genetic algorithm; data mining;

D O I：

10.1016/j.knosys.2006.03.004

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 [模式识别与智能系统]; 0812 [计算机科学与技术]; 0835 [软件工程]; 1405 [智能科学与技术];

摘要：

We present a multi-objective genetic algorithm for mining highly predictive and comprehensible classification rules from large databases. We emphasize predictive accuracy and comprehensibility of the rules. However, accuracy and comprehensibility of the rules often conflict with each other. This makes it an optimization problem that is very difficult to solve efficiently. We have proposed a multi-objective evolutionary algorithm called improved niched Pareto genetic algorithm (INPGA) for this purpose. We have compared the rule generation by INPGA with that by simple genetic algorithm (SGA) and basic niched Pareto genetic algorithm (NPGA). The experimental result confirms that our rule generation has a clear edge over SGA and NPGA. (c) 2006 Elsevier B.V. All rights reserved.

引用

页码：413 / 421

页数：9

共 21 条

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[Anonymous], ADV SOFT COMPUTING

[2]

[Anonymous], 1991, Handbook of genetic algorithms

[3]

A fast and elitist multiobjective genetic algorithm: NSGA-II [J].