A Comparison of Techniques for Handling Incomplete Input Data with a Focus on Attribute Relevance Influence

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI:10.1109/ICMLA.2010.126

M. Millán-Giraldo, J. S. Sánchez, V. Traver

引用次数: 3

Abstract

This work presents a new approach based on support vector regression to deal with incomplete input (unseen) data and compares it to other existing techniques. The empirical analysis has been done over 18 real data sets and using five different classifiers, with the aim of foreseeing which technique can be deemed as more suitable for each classifier. Also, this study tries to devise how the relevance of the missing attribute affects the performance of each pair (handling algorithm, classifier). Experimental results demonstrate that no technique is absolutely better than the others for all classifiers. However, combining the proposed strategy with the nearest neighbor classifier appears as the best choice to face the problem of missing attribute values in the input data.

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以属性关联影响为重点的不完全输入数据处理技术比较

这项工作提出了一种基于支持向量回归的新方法来处理不完整的输入(看不见的)数据，并将其与其他现有技术进行了比较。本文对18个真实数据集进行了实证分析，并使用了5种不同的分类器，目的是预测哪种技术更适合每种分类器。此外，本研究试图设计缺失属性的相关性如何影响每对(处理算法，分类器)的性能。实验结果表明，对于所有分类器，没有一种技术绝对优于其他技术。然而，将所提出的策略与最近邻分类器相结合是面对输入数据中属性值缺失问题的最佳选择。

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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2010 Ninth International Conference on Machine Learning and Applications

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