Abstract:
Classification accuracy improvement is an
essential process in data analysis problems and large
amount of data generated in extra bytes per year are
produced by many real world application. There are
many enhancement techniques to address the
problems as regarding the classification
performance, have been proposed previously by
many researchers. However, the issues of mislabeling
problems that depend on noise dataset, class
imbalanced problems and big data problems still
have been a challenge of today’s research. In data
mining process, the ensemble of classifiers are known
to increase the accuracy of single classifiers by
combining several of them, but neither of these
learning techniques alone solve the above problems.
Therefore, this paper analyzes on the problems of
data and emphasizes on the ensemble techniques by
using centroid based clustering methods. In order to
get the more accuracy, filtering process is used by
enhancing the simple ensemble ways of classifier.
