Using evolutionary sampling to mine imbalanced data

Class imbalance tends to cause inferior performance in data mining learners. Evolutionary sampling is a technique which seeks to counter this problem by using genetic algorithms to evolve a reduced sample of a complete dataset to train a classification model. Evolutionary sampling works to remove noisy and duplicate instances so that the sampled training data will produce a superior classifier. We propose this novel technique as a method to handle severe class imbalance in data mining. This paper presents our research into the the use of evolutionary sampling with C4.5 decision trees and compares the technique´s performance with random undersamp ling.