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Influence of unstable patterns in layered cluster oriented ensemble classifier

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conference contribution
posted on 2017-12-06, 00:00 authored by Ashfaqur RahmanAshfaqur Rahman, Brijesh Verma
In this paper, we have investigated the influence of cluster instability on the performance of layered cluster oriented ensemble classifier. The final contents of clusters in some clustering algorithms like k–means depend on the initialization of clustering parameters like cluster centres. Layered cluster oriented ensemble classifier is based on this philosophy where the base classifiers are trained on clusters generated at multiple layers from random initialization of cluster centres. As the data is clustered into multiple layers some patterns move between clusters (unstable patterns). This instability of patterns brings indiversity among the base classifiers that in turn influences the accuracy of the ensemble classifier. There is thus a connection between the instability of the patterns and the accuracy of layered cluster oriented ensemble classifier. The research presented in this paper aims to find this connection by investigating the influence of unstable patterns on the overall ensemble classifier accuracy as well as diversity among the base classifiers. We have provided results from a number of experiments to quantify this influence.

History

Start Page

421

End Page

428

Number of Pages

8

Start Date

2012-01-01

Finish Date

2012-01-01

ISBN-13

9781467314909

Location

Brisbane, Qld., Australia

Publisher

IEEE

Place of Publication

USA

Peer Reviewed

  • Yes

Open Access

  • No

External Author Affiliations

Centre for Intelligent and Networked Systems (CINS); Institute for Resource Industries and Sustainability (IRIS);

Era Eligible

  • Yes

Name of Conference

IEEE International Joint Conference on Neural Networks;IEEE World Congress on Computational Intelligence