A hybrid approach for classification of rare class data

Learning of rare class data is a challenging problem in field of classification process. A rare class or imbalanced class learning is the common problem faced by many real-world applications, because of this many researcher work focused on this issue. Rare class data always generate wrong results because of overwhelming accuracy of minority class by majority class. There are lots of methods been proposed to handle imbalanced class or rare class or skew class problem. This paper proposes a hybrid method, i. e. classification- and clustering-based method, solving rare class problem. This proposed hybrid method uses k-means, ensemble and divide and merge methods. This method tries to improve detection rate of every class. For experimental work, the proposed method is tested on real datasets. The experimental results show that proposed method works well as compared with other algorithms.