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Communication Dans Un Congrès Année : 2009

Application-independent feature construction from noisy samples

Résumé

When training classifiers, presence of noise can severely harm their performance. One may differentiate class noise from attribute noise. The earlier has been extensively studied while a few methods have been recently developed to handle the latter. Among the recent approaches, detecting, cleansing and correcting are the main words. In this paper, we focus on attribute noise and we consider how a frequent fault-tolerant (FFT) pattern mining task can be used to support noise-tolerant classification. Our method copes with noise without changing or removing any information from the data. It is based on an application independent strategy for feature construction based on the so-called delta-free pattern type which has been proposed earlier as an approximate condensed representations of frequent itemsets. Our experimental evaluation on noisy training data sets shows accuracy improvement when using the computed features instead of the original ones.

Dates et versions

hal-01437621 , version 1 (17-01-2017)

Identifiants

Citer

Dominique Gay, Nazha Selmaoui-Folcher, Jean-François Boulicaut. Application-independent feature construction from noisy samples. Proc. 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining PaKDD'09, Apr 2009, Bangkok, Thailand. pp.965-972, ⟨10.1007/978-3-642-01307-2_102⟩. ⟨hal-01437621⟩
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