Ensembles of diverse classifiers using synthetic training data

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 class...

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Main Authors:	Akhand, M.A.H, Shill, P.C., Rahman, M.M. Hafizur, Murase, K.
Format:	Conference or Workshop Item
Language:	English
Published:	2012
Subjects:	TK7885 Computer engineering
Online Access:	http://irep.iium.edu.my/24981/ http://irep.iium.edu.my/24981/1/1051C.pdf

id	iium-24981
recordtype	eprints
spelling	iium-249812012-09-18T02:07:16Z http://irep.iium.edu.my/24981/ Ensembles of diverse classifiers using synthetic training data Akhand, M.A.H Shill, P.C. Rahman, M.M. Hafizur Murase, K. TK7885 Computer engineering 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. 2012-07-03 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/24981/1/1051C.pdf 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.
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	TK7885 Computer engineering
spellingShingle	TK7885 Computer engineering Akhand, M.A.H Shill, P.C. Rahman, M.M. Hafizur Murase, K. Ensembles of diverse classifiers using synthetic training data
description	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.
format	Conference or Workshop Item
author	Akhand, M.A.H Shill, P.C. Rahman, M.M. Hafizur Murase, K.
author_facet	Akhand, M.A.H Shill, P.C. Rahman, M.M. Hafizur Murase, K.
author_sort	Akhand, M.A.H
title	Ensembles of diverse classifiers using synthetic training data
title_short	Ensembles of diverse classifiers using synthetic training data
title_full	Ensembles of diverse classifiers using synthetic training data
title_fullStr	Ensembles of diverse classifiers using synthetic training data
title_full_unstemmed	Ensembles of diverse classifiers using synthetic training data
title_sort	ensembles of diverse classifiers using synthetic training data
publishDate	2012
url	http://irep.iium.edu.my/24981/ http://irep.iium.edu.my/24981/1/1051C.pdf
first_indexed	2023-09-18T20:37:21Z
last_indexed	2023-09-18T20:37:21Z
_version_	1777409134717566976

Ensembles of diverse classifiers using synthetic training data

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