Evolutionary Methods for Mining Data with Class Imbalance

Class imbalance tends to cause inferior performance in data mining learners,
particularly with regard to predicting the minority class, which generally imposes
a higher misclassification cost. This work explores the benefits of using genetic
algorithms (GA) to develop classification models which are better able to deal with
the problems encountered when mining datasets which suffer from class imbalance.
Using GA we evolve configuration parameters suited for skewed datasets for three
different learners: artificial neural networks, 0 4.5 decision trees, and RIPPER. We
also propose a novel technique called evolutionary sampling which works to remove
noisy and unnecessary duplicate instances so that the sampled training data will
produce a superior classifier for the imbalanced dataset. Our GA fitness function
uses metrics appropriate for dealing with class imbalance, in particular the area
under the ROC curve. We perform extensive empirical testing on these techniques
and compare the results with seven exist ing sampling methods.