Improving Imbalanced Data Classification Accuracy by using Fuzzy Similarity Measure and Subtractive Clustering

One of the biggest challenges in this field is classification problems which refers to the number of different samples in each class. If a data set includes two classes, imbalance distribution occurs when one class has a large number of samples while the other is represented by a small number of samples. In general, the methods of solving these problems are divided into two categories: under-sampling and over-sampling. In this research, it is focused on under-sampling and the advantages of this method will be analyzed by considering the efficiency of classifying imbalanced data and it’s supposed to provide a method for sampling a majority data class by using subtractive clustering and fuzzy similarity measure. For this purpose, at first the subtractive clustering is conducted and the majority data class is clustered. Then, using fuzzy similarity measure, samples of each cluster will be ranked and appropriate samples are selected based on these rankings. The selected samples with the minority class create the final dataset. In this research, MATLAB software is used for implementation, the results are evaluated by using AUC criterion and analyzing the results has been performed by standard statistical tools. The experimental results show that the proposed method is superior to other methods of under-sampling.