A Review on Data Level Approaches for Managing Imbalanced Classification Problem

Abstract

In real world, the distribution of dataset is not in symmetric form. It can vary from application to application and distribution of data in that application. The un-symmetric form of this distribution is called imbalanced class distribution or skewed class distribution. So, the classification of data with skewed distribution of class can lead to the poor performance of the classifier. To solve the problem of imbalanced dataset in which the instances of one class is more than the instances of other class, there are different data level approaches for handling imbalanced classes. So, in this paper we will discuss about different data level approaches and have comparative study among them.