Comparison of Multi-objective Evolutionary Algorithms for prototype selection in nearest neighbor classification

G. Acampora, G. Tortora, A. Vitiello
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引用次数: 3

Abstract

The nearest neighbor classifiers are popular supervised classifiers due to their ease of use and good performance. However, in spite of their success, they suffer from some defects such as high storage requirements, high computational complexity, and low noise tolerance. In order to address these drawbacks, prototype selection has been studied as a technique to reduce the size of training datasets without deprecating the classification accuracy. Due to the need of achieving a trade-off between accuracy and reduction, Multi-Objective Evolutionary Algorithms (MOEAs) are emerging as methods efficient in solving the prototype selection problem. The goal of this paper is to perform a systematic comparison among well-known MOEAs in order to study their effects in solving this problem. The comparison involves the study of MOEAs' performance in terms of the well-known measures such as hypervolume, Δ index and coverage of two sets. The empirical analysis of the experimental results is validated through a statistical multiple comparison procedure.
基于多目标进化算法的最近邻分类原型选择比较
最近邻分类器由于其易于使用和良好的性能而成为流行的监督分类器。然而,尽管它们取得了成功,但也存在存储要求高、计算复杂度高、噪声容忍度低等缺陷。为了解决这些缺点,原型选择被研究作为一种技术来减少训练数据集的大小而不影响分类精度。由于需要在精度和约简之间取得平衡,多目标进化算法(moea)成为解决原型选择问题的有效方法。本文的目的是对知名的moea进行系统比较,以研究它们在解决这一问题方面的效果。比较涉及使用hypervolume、Δ指数和两组覆盖率等众所周知的度量来研究moea的性能。通过统计多元比较对实验结果进行了实证分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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