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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/24981/1/1051C.pdf
http://irep.iium.edu.my/24981/
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spelling my.iium.irep.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 REM 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.
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
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/24981/1/1051C.pdf
http://irep.iium.edu.my/24981/
_version_ 1643608852369768448
score 13.160551

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