標題: Design of nearest neighbor classifiers: multi-objective approach
作者: Chen, JH
Chen, HM
Ho, SY
生物資訊及系統生物研究所
Institude of Bioinformatics and Systems Biology
關鍵字: nearest neighbor classifiers;genetic algorithm;multi-objective optimization;feature selection;minimum reference set
公開日期: 1-七月-2005
摘要: The goal of designing optimal nearest neighbor classifiers is to maximize classification accuracy while minimizing the sizes of both reference and feature sets. A usual way is to adaptively weight the three objectives as an objective function and then use a single-objective optimization method for achieving this goal. This paper proposes a multi-objective approach to cope with the weight tuning problem for practitioners. A novel intelligent multi-objective evolutionary algorithm IMOEA is utilized to simultaneously edit compact reference and feature sets for nearest neighbor classification. Three comparison studies are designed to evaluate performance of the proposed approach. It is shown empirically that the IMOEA-designed classifiers have high classification accuracy and small sizes of reference and feature sets. Moreover, IMOEA can provide a set of good solutions for practitioners to choose from in a single run. The simulation results indicate that the IMOEA-based approach is an expedient method to design nearest neighbor classifiers, compared with an existing single-objective approach. (c) 2005 Elsevier Inc. All rights reserved.
URI: http://dx.doi.org/10.1016/j.ijar.2004.11.009
http://hdl.handle.net/11536/13524
ISSN: 0888-613X
DOI: 10.1016/j.ijar.2004.11.009
期刊: INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
Volume: 40
Issue: 1-2
起始頁: 3
結束頁: 22
顯示於類別:期刊論文


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  1. 000229981300002.pdf