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Non–uniform layered clustering for ensemble classifier generation and optimality

conference contribution
posted on 06.12.2017, 00:00 authored by Ashfaqur RahmanAshfaqur Rahman, X Yao, Brijesh VermaBrijesh Verma
In this paper we present an approach to generate ensemble of classifiers using non–uniform layered clustering. In the proposed approach the dataset is partitioned into variable number of clusters at different layers. A set of base classifiers is trained on the clusters at different layers. The decision on a pattern at each layer is obtained from the classifier trained on the nearest cluster and the decisions from the different layers are fused using majority voting to obtain the final verdict. The proposed approach provides a mechanism to obtain the optimal number of layers and clusters using Genetic Algorithm. Clustering identifies difficult–to–classify patterns and layered non–uniform clustering approach brings in diversity among the base classifiers at different layers. The proposed method performs relatively better than the other state–of–art ensemble classifier generation methods as evidenced from the experimental results.

History

Start Page

551

End Page

558

Number of Pages

8

Start Date

01/01/2010

Finish Date

01/01/2010

eISSN

1611-3349

ISSN

0302-9743

ISBN-13

9783642175336

Location

Sydney, Australia

Publisher

Springer

Place of Publication

Heidelberg, Germany

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

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

Era Eligible

Yes

Name of Conference

ICONIP (Conference)