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Classification by Cluster Analysis: A New Meta-Learning Based Approach

Jurek, Anna, Bi, Yaxin, Wu, Shengli and Nugent, Chris D. (2011) Classification by Cluster Analysis: A New Meta-Learning Based Approach. In: Proceeding MCS'11 Proceedings of the 10th international conference on Multiple classifier systems. Springer-Verlag Berlin, pp. 259-268. ISBN 978-3-642-21556-8 [Book section]

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Abstract

Combination of multiple classifiers, commonly referred to as an classifier ensemble, has previously demonstrated the ability to improve classification accuracy in many application domains. One popular approach to building such a combination of classifiers is known as stacking and is based on a meta-learning approach. In this work we investigate a modified version of stacking based on cluster analysis. Instances from a validation set are firstly classified by all base classifiers. The classified results are then grouped into a number of clusters. Two instances are considered as being similar if they are correctly/incorrectly classified to the same class by the same group of classifiers. When classifying a new instance, the approach attempts to find the cluster to which it is closest. The method outperformed individual classifiers, classification by a clustering method and the majority voting method.

Item Type:Book section
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Research Institutes and Groups:Computer Science Research Institute > Smart Environments
Computer Science Research Institute
Computer Science Research Institute > Artificial Intelligence and Applications
ID Code:25483
Deposited By: Dr Yaxin Bi
Deposited On:19 Jan 2016 09:38
Last Modified:19 Jan 2016 09:38

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