Attribute Selection Methods for Filtered Attribute Subspace based Bagging with Injected Randomness (FASBIR)

IM Whittley, AJ Bagnall, L Bull, M Pettipher, M Studley, F Tekiner

Research output: Contribution to conferencePaper

Abstract

Filtered Attribute Subspace based Bagging with Injected Randomness (FASBIR) is a recently proposed algorithm for ensembles of k-nn classifiers [28]. FASBIR works by first performing a global filtering of attributes using information gain, then randomising the bagged ensemble with random subsets of the remaining attributes and random distance metrics. In this paper we propose two refinements of FASBIR and evaluate them on several very large data sets.
Original languageEnglish
Pages10-17
Number of pages8
Publication statusPublished - 2006
EventFeature Selection for Data Mining Workshop, Part of the 2006 SIAM Conference on Data Mining - Bethesda, Maryland
Duration: 22 Apr 2006 → …

Conference

ConferenceFeature Selection for Data Mining Workshop, Part of the 2006 SIAM Conference on Data Mining
CityBethesda, Maryland
Period22/04/06 → …

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