Data reduction based on artificial immune system

被引：5

作者：

Gong, Mao-Guo ^{[1
]}

Hao, Lin ^{[1
]}

Jiao, Li-Cheng ^{[1
]}

Wang, Xiao-Hua ^{[1
]}

Sun, Yi-Fei ^{[1
]}

机构：

[1] Key Laboratory of Intelligent Perception and Image Understanding, Institute of Intelligent Information Processing, Xidian University

来源：

Ruan Jian Xue Bao/Journal of Software | 2009年 / 20卷 / 04期

关键词：

Artificial immune system; Clonal selection; Data reduction; Evolutionary computation; Instance selection;

D O I：

10.3724/SP.J.1001.2009.03252

中图分类号：

学科分类号：

摘要：

Based on the antibody clonal selection theory, an immune clonal data reduction algorithm is proposed for instance selection problems of data reduction. The theory of Markov chain proves that the new algorithm is convergent with probability 1. The experimental studies on seven standard data sets of UCI repository show that the algorithm proposed in this paper is effective. The best domain of the weight parameter λ is determined by analyzing its effect on algorithm's performance. Furthermore, an encoding method based on the stratified strategy is introduced to accelerate the convergence speed when solving large scale data reduction problems. The experimental studies based on seven large scale data sets show that the improved method is superior to the primary one. Finally, the best domain of the number of stratums t is determined by analyzing its effect on algorithm's performance based on the data sets Letter and DNA. © by Institute of Software, the Chinese Academy of Sciences.

引用

页码：804 / 814

页数：10

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