A Novel Class Imbalance Learning Using Ordering Points Clustering
In Data mining and Knowledge Discovery hidden and valuable knowledge from the data sources is discovered. The traditional algorithms used for knowledge discovery are bottle necked due to wide range of data sources availability. Class imbalance is a one of the problem arises due to data source which provide unequal class i.e. examples of one class in a training data set vastly outnumber examples of the other classes. This paper proposes a method belonging to under sampling approach which uses OPTICS one of the best visualization clustering technique for handling class imbalance problem. In the proposed approach, further Classification of new data is performed by applying C4.5 algorithm as the base algorithm.