Redundant feature elimination for multi-class problems

A Appice, M Ceci, S Rawles, PA Flach

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

278 Citations (Scopus)

Abstract

We consider the problem of eliminating redundant Boolean features for a given data set, where a feature is redundant if it separates the classes less well than another feature or set of features. Lavrac et al. proposed the algorithm REDUCE that works by pairwise comparison of features, i.e., it eliminates a feature if it is redundant with respect to another feature. Their algorithm operates in an ILP setting and is restricted to two-class problems. In this paper we improve their method and extend it to multiple classes. Central to our approach is the notion of a neighbourhood of examples: a set of examples of the same class where the number of different features between examples is relatively small. Redundant features are eliminated by applying a revised version of the REDUCE method to each pair of neighbourhoods of different class. We analyse the performance of our method on a range of data sets.
Translated title of the contributionRedundant feature elimination for multi-class problems
Original languageEnglish
Title of host publicationUnknown
EditorsRuss Greiner, Dale Schuurmans
PublisherAssociation for Computing Machinery (ACM)
Pages33 - 40
Number of pages7
ISBN (Print)1581138385
Publication statusPublished - Jul 2004

Bibliographical note

Conference Proceedings/Title of Journal: Proceedings of the 21st International Conference on Machine Learning (ICML 2004)

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