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Dimensionality Reduction through Correspondence Analysis

Dimensionality Reduction through Correspondence Analysis
Dimensionality Reduction through Correspondence Analysis
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.
s.n.
Payne, Terry R.
09574f42-3cbc-43d3-86c2-2f96eb6319a0
Edwards, Peter
5ee73a94-75a0-426f-ab1b-ce918b06a1ea
Payne, Terry R.
09574f42-3cbc-43d3-86c2-2f96eb6319a0
Edwards, Peter
5ee73a94-75a0-426f-ab1b-ce918b06a1ea

Payne, Terry R. and Edwards, Peter (1999) Dimensionality Reduction through Correspondence Analysis s.n.

Record type: Monograph (Project Report)

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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Published date: 1999
Organisations: Electronics & Computer Science

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Local EPrints ID: 263091
URI: http://eprints.soton.ac.uk/id/eprint/263091
PURE UUID: 332d159c-d19f-46f0-ae93-9d799a226003

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Date deposited: 09 Oct 2006
Last modified: 23 Sep 2020 16:43

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Contributors

Author: Terry R. Payne
Author: Peter Edwards

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