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: email@example.com).
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.