为分类器训练删除或保留异常值?

2014 Sixth World Congress on Nature and Biologically Inspired Computing (NaBIC 2014) Pub Date : 2014-10-16 DOI:10.1109/NaBIC.2014.6921892

A. J. Tallón-Ballesteros, José Cristóbal Riquelme Santos

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引用次数: 18

摘要

本文介绍了两种分类的统计离群值检测方法。在二值和多类分类问题上的实验表明，对于C4.5和1近邻分类器，局部去除异常值显著提高了一个或两个性能指标。此外，根据异常值的数量，提出了问题的分类方法。

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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Deleting or keeping outliers for classifier training?

This paper introduces two statistical outlier detection approaches by classes. Experiments on binary and multi-class classification problems reveal that the partial removal of outliers improves significantly one or two performance measures for C4.5 and 1-nearest neighbour classifiers. Also, a taxonomy of problems according to the amount of outliers is proposed.

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2014 Sixth World Congress on Nature and Biologically Inspired Computing (NaBIC 2014)

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