Efficient and Effective Feature Selection in the Presence of Feature Interaction and Noise

D. Partridge, W. Wang, P. Jones

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Citation (Scopus)


This paper addresses the problem of feature subset selection for classification tasks. In particular, it focuses on the initial stages of complex realworld classification tasks when feature interaction is expected but illunderstood, and noise contaminating actual feature vectors must be expected to further complicate the classification problem. A neural-network based featureranking technique, the ‘clamping’ technique, is proposed as a robust and effective basis for feature selection that is more efficient than the established comparable techniques of sequential floating searches. The efficiency gain is that of an Order(n) algorithm over the Order(n2) floating search techniques. These claims are supported by an empirical study of a complex classification task.
Original languageEnglish
Title of host publicationAdvances in Pattern Recognition — ICAPR 2001
EditorsSameer Singh, Nabeel Murshed, Walter Kropatsch
PublisherSpringer Berlin / Heidelberg
Number of pages6
ISBN (Print)978-3-540-41767-5
Publication statusPublished - 2001
EventSecond International Conference - Rio de Janeiro, Brazil
Duration: 11 Mar 200114 Mar 2001

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin / Heidelberg


ConferenceSecond International Conference
CityRio de Janeiro

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