File(s) not publicly available

Cluster oriented ensemble classifiers using multi-objective evolutionary algorithm

conference contribution
posted on 06.12.2017, 00:00 by Ashfaqur RahmanAshfaqur Rahman, Brijesh VermaBrijesh Verma
In this paper, we present an application of Multi–Objective Evolutionary Algorithm (MOEA) for generating cluster oriented ensemble classifier. In our recently developed Non–Uniform Layered Cluster Oriented Ensemble Classifier (NULCOEC), the data set is partitioned into a variable number of clusters at different layers. Base classifiers are then trained on the clusters at different layers. The performance of NULCOEC is a function of the vector (layers,clusters) and the research presented in this paper investigates the implication of applying MOEA to generate NULCOEC. Accuracy and diversity of the ensemble classifier is expressed as a function of layers and clusters. A MOEA then searches for the combination of layers and clusters to obtain the non–dominated set of (accuracy,diversity). We have also obtained the results of single objective optimization (i.e. optimizing either accuracy or diversity) and compared them with the results of MOEA. The results show that the MOEA can improve the performance of ensemble classifier.

Funding

Category 3 - Industry and Other Research Income

History

Start Page

1

End Page

6

Number of Pages

6

Start Date

01/01/2013

Finish Date

01/01/2013

ISBN-13

9781467361286

Location

Dallas, Texas, USA

Publisher

IEEE

Place of Publication

Piscataway, NJ

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

CSIRO ICT Centre; Centre for Intelligent and Networked Systems (CINS); TBA Research Institute;

Era Eligible

Yes

Name of Conference

IEEE International Joint Conference on Neural Networks