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Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2013 Vol. 3(1): 137-141 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2013.V3.288

A Novel Approach for Generating Clustered Based Ensemble of Classifiers

Mohammad Raihanul Islam, Md. Mustafizur Rahman, Asif Salekin, and Ahmed Shayer Andalib
Abstract—In this paper, we have presented a novel concept for constructing ensemble of classifiers. Here, we have considered a situation where data is available over the period of time. If enough remotely located data points are available at the classification system, the current system may not cope with newer data instances. In that case, existing settings or parameters of the classifiers need to be modified to act properly on newer instances. In this paper, we have presented a general technique for detecting enough remotely located data points arrived at the classifiers so that existing classification model can longer suitable for the new situation and proposed a change of settings to cope with the newer situation. We have performed detail analysis of our approach. Our approach has showed satisfactory results in dynamic environments.

Index Terms—Neural networks, ensemble of classifiers, k-means clustering, chunk of data, identical classifier.

The authors are with Bangladesh University of Engineering and Technology, Dhaka, Bangladesh (e-mail: kash_shaf91@yahoo.com).

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Cite:Mohammad Raihanul Islam, Md. Mustafizur Rahman, Asif Salekin, and Ahmed Shayer Andalib, "A Novel Approach for Generating Clustered Based Ensemble of Classifiers," International Journal of Machine Learning and Computing vol. 3, no. 1, pp. 137-141, 2013.

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