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

Whittley, IM, Bagnall, AJ, Bull, L, Pettipher, M, Studley, M and Tekiner, F (2006) Attribute Selection Methods for Filtered Attribute Subspace based Bagging with Injected Randomness (FASBIR). In: Feature Selection for Data Mining Workshop, Part of the 2006 SIAM Conference on Data Mining, 2006-04-22.

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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.

Item Type:	Conference or Workshop Item (Paper)
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Data Science and Statistics
Depositing User:	Vishal Gautam
Date Deposited:	24 Jun 2011 08:30
Last Modified:	14 Feb 2023 15:31
URI:	https://ueaeprints.uea.ac.uk/id/eprint/23613
DOI:

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