Class Imbalance Problem in Data Mining Review

TL;DR

This paper reviews the class imbalance problem in data mining, discussing its challenges, existing approaches, and providing a systematic analysis to guide future research efforts.

Contribution

It offers a comprehensive review and systematic analysis of methods addressing class imbalance, highlighting their advantages and disadvantages for future research.

Findings

Different approaches have unique strengths and weaknesses.

Class imbalance significantly affects classification accuracy.

Guidance for future research directions in class imbalance.

Abstract

In last few years there are major changes and evolution has been done on classification of data. As the application area of technology is increases the size of data also increases. Classification of data becomes difficult because of unbounded size and imbalance nature of data. Class imbalance problem become greatest issue in data mining. Imbalance problem occur where one of the two classes having more sample than other classes. The most of algorithm are more focusing on classification of major sample while ignoring or misclassifying minority sample. The minority samples are those that rarely occur but very important. There are different methods available for classification of imbalance data set which is divided into three main categories, the algorithmic approach, data-preprocessing approach and feature selection approach. Each of this technique has their own advantages and disadvantages. In this paper systematic study of each approach is define which gives the right direction for research in class imbalance problem.