An intelligent undersampling technique based upon intuitionistic fuzzy sets to alleviate class imbalance problem of classification with noisy environment

摘要

Traditional classification algorithms (TCA) do not work with the unequal class sizes. There are applications wherein the requirement is to discover the exceptional/rare cases such as frauds in credit card database or fraudulent mobile calls, etc. TCA, when applied in such cases, failed to detect rare cases. This is stated as the problem of imbalance classes. The problem is more serious when TCA are applied on the data distribution having other impurities like noise, overlapping classes and imbalance within classes. This paper presented an intelligent undersampling and ensemble based classification method to resolve the problem of imbalanced classes in noisy situation. A synthetic dataset with different extent of noise is used to assess the classification performance of the proposed techniques. The results indicate that the presented undersampling and ensemble based classifier techniques has better classification performance in noisy situation when we compare them with RUS and SMOTE having classifiers like C4.5, RIPPLE, KNN, SVM, MLP, NaiveBayes and with the ensemble techniques like boosting, bagging and randomforest.