Abstract

Real world application often found the problem of unbalanced dataset. This then create the problem in machine learning methods . In this paper we have surveyed the imbalance dataset problem at the algorithmic level . By over sampling and under sampling some researchers artificially prove that updated svm ,cost sensitive classifier ,class orientation methods can be good on imbalanced dataset. This imbalance problem is also switching towards hybrid algorithm.

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