Application-Independent Feature Construction from Noisy Samples ⋆

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Application-Independent Feature Construction from Noisy Samples ⋆

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by Dominique Gay , Nazha Selmaoui , Jean-françois Boulicaut

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BibTeX

@MISC{Gay_application-independentfeature,
    author = {Dominique Gay and Nazha Selmaoui and Jean-françois Boulicaut},
    title = {Application-Independent Feature Construction from Noisy Samples ⋆},
    year = {}
}

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Abstract

Abstract. When training classifiers, presence of noise can severely harm their performance. In this paper, we focus on “non-class ” attribute noise and we consider how a frequent fault-tolerant (FFT) pattern mining task can be used to support noise-tolerant classification. Our method is based on an application independent strategy for feature construction based on the so-called δ-free patterns. Our experiments on noisy training data shows accuracy improvement when using the computed features instead of the original ones. 1

Keyphrases

noisy sample application-independent feature construction original one application independent strategy feature construction accuracy improvement so-called free pattern non-class attribute noise noise-tolerant classification noisy training data computed feature

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