Design of nearest neighbor classifiers using an intelligent multi-objective evolutionary algorithm

Jian Hung Chen*, Hung Ming Chen, Shinn-Ying Ho

*Corresponding author for this work

研究成果: Conference article同行評審

4 引文 斯高帕斯(Scopus)


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. Two 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.

頁(從 - 到)262-271
期刊Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
出版狀態Published - 1 十二月 2004
事件8th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2004: Trends in Artificial Intelligence - Auckland, New Zealand
持續時間: 9 八月 200413 八月 2004

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