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Snehlata S. Dongre
Latesh G. Malik


Class imbalance problem is getting so much attention of researchers now a days. In real life there are number of applications that generates imbalanced data sets. Imbalance nature of data makes classification task difficult. Dealing with these kinds of imbalanced dataset is the one of the biggest challenge in the data mining. Imbalanced dataset means the ratio of positive and negative classes is not balanced. The class that is having more number of samples is known as majority class and the class that is having less number of samples is known as minority class samples. Minority class samples are less but important. In the classification task, most of the times, we are ignoring minority class samples and more concentrating on majority class samples. This leads to good overall accuracy but poor minority class detection rate. Many algorithms have been proposed to deal with the imbalanced data problem but each has its prons and corns. Different techniques used for handling imbalance data are discussed here.


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Author Biographies

Snehlata S. Dongre, PhD Scholar, Computer Science and Engineering Department, GHRCE Nagpur, India

Computer Science and Engineering Department, PhD Scholar

Latesh G. Malik, Associate Professor, Computer Science and Engineering Department, Govt. Engineering College, Nagpur, India

Computer Science and Engineering Department, Associate Professor


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