Dimensionality Reduction through Correspondence Analysis

Abstract

Many learning algorithms make an implicit assumption that all the attributes of the presented data are relevant to a learning task. However, several studies on attribute selection have demonstrated that this assumption rarely holds. In addition, for many supervised learning algorithms such as nearest neighbour algorithms, the inclusion of irrelevant attributes can result in a degradation in the classification accuracy of the learning algorithm. Whilst a number of different methods for attribute selection exist, many of these are only appropriate for datasets which contain a small number of attributes (e.g. less than 20). This paper presents an alternative approach to attribute selection, which can be applied to datasets with a greater number of attributes. We present an evaluation of the approach which contrasts its performance with one other attribute selection technique.

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