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Ensembles of diverse classifiers using synthetic training data

Akhand, M.A.H and Shill, P.C. and Rahman, M.M. Hafizur and Murase, K. (2012) Ensembles of diverse classifiers using synthetic training data. In: International Conference on Computer and Communication Engineering (ICCCE 2012), 3-5 July 2012, Seri Pacific Hotel Kuala Lumpur.

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Abstract

The goal of an ensemble construction with several classifiers is to achieve better generalization than that of a single classifier. And proper diversity among classifiers is considered as the condition for an ensemble construction. This paper investigates synthetic pattern for diversity among classifiers. It alters input feature values of some patterns with the values of other patterns to get synthetic patterns. The pattern generation from using exiting patterns seems emphasize both accuracy and diversity among individual classifiers. Ensemble based on the synthetic patterns is evaluated for both neural networks and decision trees on a set of benchmark problems and was found to show good generalization ability.

Item Type: Conference or Workshop Item (Full Paper)
Uncontrolled Keywords: ensemble of classifiers; diversity; generalization; synthetic pattern.
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices > TK7885 Computer engineering
Kulliyyahs/Centres/Divisions/Institutes: Kulliyyah of Information and Communication Technology > Department of Computer Science
Kulliyyah of Information and Communication Technology > Department of Computer Science
Depositing User: Dr. M.M. Hafizur Rahman
Date Deposited: 07 Sep 2012 13:49
Last Modified: 18 Sep 2012 10:07
URI: http://irep.iium.edu.my/id/eprint/24981

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