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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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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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