Super computer heterogeneous classifier meta-ensembles

Bagnall, Anthony J., Cawley, Gavin C. ORCID: https://orcid.org/0000-0002-4118-9095, Whittley, Ian M., Bull, Larry, Studley, Matthew, Pettipher, Mike and Tekiner, F. (2007) Super computer heterogeneous classifier meta-ensembles. International Journal of Data Warehousing and Mining (IJDWM), 3 (2). pp. 67-82. ISSN 1548-3924

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Abstract

This article describes the entry of the Super Computer Data Mining (SCDM) Project to the 10th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2006 Data Mining Competition. The SCDM project is developing data mining tools for parallel execution on Linux clusters. The code is freely available; please contact the first author for a copy. We combine several classifiers, some of them ensemble techniques, into a heterogeneous meta-ensemble, to produce a probability estimate for each test case. We then use a simple decision theoretic framework to form a classification. The meta-ensemble contains a Bayesian neural network, a learning classifier system (LCS), attribute selection based-ensemble algorithms (Filtered At-tribute Subspace based Bagging with Injected Randomness [FASBIR]), and more well-known classifiers such as logistic regression, Naive Bayes (NB), and C4.5.

Item Type:	Article
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Data Science and AI Faculty of Science > Research Groups > Centre for Ocean and Atmospheric Sciences Faculty of Science > Research Groups > Computational Biology
Depositing User:	Vishal Gautam
Date Deposited:	28 Feb 2011 20:02
Last Modified:	24 Sep 2024 09:38
URI:	https://ueaeprints.uea.ac.uk/id/eprint/22350
DOI:	10.4018/jdwm.2007040106

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