Classification algorithm sensitivity to training data with non representative attribute noise

doi:10.1016/j.dss.2008.11.021

Classification algorithm sensitivity to training data with non representative attribute noise

2009-02-01

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DOI：10.1016/j.dss.2008.11.021

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Abstract

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We present an empirical comparison of classification algorithms when training data contains attribute noise levels not representative of field data. To study algorithm sensitivity, we develop an innovative experimental design using noise situation, algorithm, noise level, and training set size as factors. Our results contradict conventional wisdom indicating that investments to achieve representative noise levels may not be worthwhile. ill general, over representative training noise Should be avoided while under representative training noise is less of a concern. However, interactions among algorithm, noise level, and training set size indicate that these general results may not apply to particular practice situations. (c) 2008 Elsevier B.V. All rights reserved.

Keywords:

Attribute noise

Area under the Receiver Operating Curve

Classification algorithm

Classification algorithm sensitivity to training data with non representative attribute noise

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