A New approach for Classification of Highly Imbalanced Datasets using Evolutionary Algorithms

Today’s most of the research interest is in the application of evolutionary algorithms. One of the examples is classification rules in imbalanced domains. The problem of Imbalanced data sets plays a major challenge in data mining community. In imbalanced data sets, the number of instances of one class is much higher than the others, and the class of fewer representatives is of more interest from the point of the learning task. Traditional Machine Learning algorithms work well with balanced data sets, but not able to deal with classification of imbalanced data sets. In the present paper we use different operators of Genetic Algorithms (GA) for over-sampling to enlarge the ratio of positive samples, and then apply clustering to the over-sampled training dataset as a data cleaning method for both classes, removing the redundant or noisy samples. The proposed approach was experimentally analyzed and the experimental results shows an improvement in the classification measured as the area under the receiver operating characteristics (ROC) curve.