Abstract—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.
Index Terms—Decision tree ensemble, diversity, feature values modification, generalization, pattern generation.
M. A. H. Akhand is with the Dept. of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna-9203, Bangladesh (e-mail: firstname.lastname@example.org). M. M. Hafizur Rahman is with Dept. of Computer Science, KICT, International Islamic University Malaysia, Jalan Gombak, 50728 Selayang, Selangor, Malaysia (e-mail: email@example.com). K. Murase is with Graduate School of Engineering, University of Fukui, 3-9-1 Bunkyo, Fukui 910-8507, Japan (e-mail: firstname.lastname@example.org).
Cite:M. A. H. Akhand, M. M. Hafizur Rahman, and K. Murase, "Pattern Generation through Feature Values Modification and Decision Tree Ensemble Construction," International Journal of Machine Learning and Computing vol.3, no. 4, pp. 322-331, 2013.