Pattern generation through feature values modification and decision tree ensemble construction
An ensemble method produces diverse classifiers and combines their decisions for ensemble’s decision. A number of methods have been investigated for constructing ensemble in which some of them train classifiers with the generated patterns. This study investigates a new technique of training patter...
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IACSIT Press
2013
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| Online Access: | http://irep.iium.edu.my/31742/ http://irep.iium.edu.my/31742/ http://irep.iium.edu.my/31742/ http://irep.iium.edu.my/31742/4/IJMLC_2013.pdf |
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iium-317422013-10-04T07:27:36Z http://irep.iium.edu.my/31742/ Pattern generation through feature values modification and decision tree ensemble construction Akhand, M. A. H Rahman, M.M. Hafizur Murase, K. TK Electrical engineering. Electronics Nuclear engineering TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices An ensemble method produces diverse classifiers and combines their decisions for ensemble’s decision. A number of methods have been investigated for constructing ensemble in which some of them train classifiers with the generated patterns. This study investigates a new technique of training pattern generation that is easy and effective for ensemble construction. The method modifies feature values of some patterns with the values of other patterns to generate different patterns for different classifiers. The ensemble of decision trees based on the proposed technique was evaluated using a suite of 30 benchmark classification problems, and was found to achieve performance better than or competitive with related conventional methods. Furthermore, two different hybrid ensemble methods have been investigated incorporating the proposed technique of pattern generation with two popular ensemble methods bagging and random subspace method (RSM). It is found that the performance of bagging and RSM algorithms can be improved by incorporating feature values modification with their training processes. Experimental investigation of different types of modification techniques finds that feature values modification with pattern values in the same class is better for generalization. IACSIT Press 2013 Article PeerReviewed application/pdf en http://irep.iium.edu.my/31742/4/IJMLC_2013.pdf Akhand, M. A. H and Rahman, M.M. Hafizur and Murase, K. (2013) Pattern generation through feature values modification and decision tree ensemble construction. International Journal of Machine Learning and Computing (IJMLC), 3 (4). pp. 322-331. ISSN 2010-3700 http://www.ijmlc.org/index.php?m=content&c=index&a=show&catid=39&id=362 10.7763/IJMLC.2013.V3.331 |
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Digital Repository |
| institution_category |
Local University |
| institution |
International Islamic University Malaysia |
| building |
IIUM Repository |
| collection |
Online Access |
| language |
English |
| topic |
TK Electrical engineering. Electronics Nuclear engineering TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices |
| spellingShingle |
TK Electrical engineering. Electronics Nuclear engineering TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices Akhand, M. A. H Rahman, M.M. Hafizur Murase, K. Pattern generation through feature values modification and decision tree ensemble construction |
| description |
An ensemble method produces diverse classifiers and combines their decisions for ensemble’s decision. A number
of methods have been investigated for constructing ensemble in which some of them train classifiers with the generated
patterns. This study investigates a new technique of training pattern generation that is easy and effective for ensemble construction. The method modifies feature values of some patterns with the values of other patterns to generate different patterns for different classifiers. The ensemble of decision trees based on the proposed technique was evaluated using a suite of 30 benchmark classification problems, and was found to achieve performance better than or competitive with related conventional methods. Furthermore, two different hybrid ensemble methods have been investigated incorporating the proposed technique of pattern generation with two popular ensemble methods bagging and random subspace method (RSM). It is found that the performance of bagging and RSM algorithms can be improved by incorporating feature values modification with their training processes. Experimental investigation of different types of modification techniques finds that feature values modification with pattern values in the same class is better for generalization. |
| format |
Article |
| author |
Akhand, M. A. H Rahman, M.M. Hafizur Murase, K. |
| author_facet |
Akhand, M. A. H Rahman, M.M. Hafizur Murase, K. |
| author_sort |
Akhand, M. A. H |
| title |
Pattern generation through feature values modification
and decision tree ensemble construction |
| title_short |
Pattern generation through feature values modification
and decision tree ensemble construction |
| title_full |
Pattern generation through feature values modification
and decision tree ensemble construction |
| title_fullStr |
Pattern generation through feature values modification
and decision tree ensemble construction |
| title_full_unstemmed |
Pattern generation through feature values modification
and decision tree ensemble construction |
| title_sort |
pattern generation through feature values modification
and decision tree ensemble construction |
| publisher |
IACSIT Press |
| publishDate |
2013 |
| url |
http://irep.iium.edu.my/31742/ http://irep.iium.edu.my/31742/ http://irep.iium.edu.my/31742/ http://irep.iium.edu.my/31742/4/IJMLC_2013.pdf |
| first_indexed |
2023-09-18T20:45:53Z |
| last_indexed |
2023-09-18T20:45:53Z |
| _version_ |
1777409671311654912 |