Feature Selection via Immune Memory Clone

朱虎明; 焦李成

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Feature Selection via Immune Memory Clone

更新时间：2025-12-08

- Feature Selection via Immune Memory Clone
- Vol. 42, Issue 6, Pages: 679-682+722(2008)
- 作者机构：
  
  西安电子科技大学智能信息处理研究所,西安,710049
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP18
- Online First：09 June 2008，
  
  Published：2008
- 稿件说明：
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朱虎明, 焦李成. Feature Selection via Immune Memory Clone[J]. 2008, 42(6): 679-682+722.
DOI：

朱虎明, 焦李成. Feature Selection via Immune Memory Clone[J]. 2008, 42(6): 679-682+722. DOI：

摘要

针对数据挖掘和模式识别等领域的高维数据降维问题

提出了一种基于抗体克隆选择学说和免疫记忆理论的特征选择算法.该算法利用抗体种群进行全局搜索

通过设立记忆单元来保留历史最好个体

并对其嵌入可控制搜索深度的局部搜索算子

用以加快抗体亲和力成熟速度

同时对抗体种群和记忆单元采用不同的亲和度函数以获得更好的搜索能力.将该算法用于几个高维数据集进行特征子集选择

然后进行最近邻分类并采用留一法验证

结果表明

与标准遗传算法相比

新算法具有更低的复杂度和更好的搜索能力

其鲁棒性也优于经典的串行浮点前向搜索算法.

Abstract

Focusing on the problem of dimension reduction in data mining and pattern recognition

a novel algorithm for feature selection was proposed based on antibody clonal selection and immune memory principle(ICMFS). The antibody population is used for global search

and the memory unit

which only reserves the best individuals

with embedded local search operations is designed for fine-tune search. Different fitness functions for the antibody population and the memory unit are used to improve search performance. The fitness of an individual is determined by evaluating the nearest neighbor classifier with leave-one-out cross-validation. Experiment results on several standard high-dimension datasets show that the proposed algorithm outperforms a conventional genetic algorithm and the classical sequential floating forward search algorithm in terms of classification accuracy and robustness.

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Keywords

references

RAYMER M L, PUNCH W F, GOODMAN E D,et al. Dimensionality reduction using genetic algorithms [J]. IEEE Trans Evolutionary Computation,2000,4(2): 164-171.

张向荣,焦李成. 基于免疫克隆选择算法的特征选择[J].复旦学报(自然科学版), 2004,43(5): 926-929.

ZHANG Xiangrong, JIAO Licheng. Feature selection based on immune clonal selection algorithm [J]. Journal of Fudan University(Natural Science), 2004,43(5): 926-929.

DASH M, LIU H. Feature selection for classification. [J]. Intelligent Data Analysis, 1997,1(3): 131-156.

杜海峰,公茂果,焦李成,等.用于高维函数优化的免疫记忆克隆规划算法 [J].自然科学进展,2004,14(8): 925-933.

DU Haifeng, GONG Maoguo, JIAO Licheng, et al. Immune memory clonal programming algorithm for high-dimensional function numerical optimization [J]. Progress in Natural Science, 2004,14(8): 925-933.

BLAKE C L,KEOGH E,MERZ C J. UCI repository of machine learning databases [M]. Irvine,CA, USA: University of California,1998.

OH I S, LEE J S, MOON B R. Hybrid genetic algorithms for feature selection [J]. IEEE Trans on Pattern Analysis and Machine Intelligence,2004,26(11): 1424-1437.

VLACHOS M, DOMENICONI C, GUNOPULOS D, et al. Non-linear dimensionality reduction techniques for classification and visualization [C]∥Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM Press, 2002: 645-651.

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