Handling Imbalance Class Problem Using Ensemble Classifier

Authors : Snehlata S. Dongre, Dr. L. G. Malik, Achamma Thomas,

Volume/Issue : Volume 2 - 2017, Issue 12 - December

Google Scholar : https://goo.gl/DF9R4u

Scribd : https://goo.gl/rgLbYu

Thomson Reuters ResearcherID : https://goo.gl/3bkzwv

Rare class problem become very popular as many researchers focused to this area. Many applications generate imbalanced datasets in real life. In imbalanced data the ratio of various class samples are not balanced. Classification becomes difficult because of this imbalanced nature of data. Handling rare class problem is an issue in the Data mining. More number of samples belonged to the class is termed as Majority class and less number of sample belonged to the class named as minority class. Sometimes the classification is biased towards majority class samples and ignoring the minority class samples. Because of this the overall accuracy may be good but the class wise accuracy is poor. Various techniques for handling rare class problem have discussed. In this paper, an algorithm Ensemble Boosting Classifier has been proposed for handling rare class problem. Algorithm has been tested for real imbalance datasets and results are good.

Keywords : Class Imbalanced Problem, Skewed Data, Rare Class Problem, Data Mining


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