Research on Improved Random Forest Algorithm for Highly Unbalanced Data

Abstract

To address the problems of low prediction performance of minority class samples due to highly unbalanced data and improved prediction performance of minority class but decreased prediction performance of majority class after balanced training set, an improved algorithm combining feature fusion and random forests quantile classifier is proposed. The algorithm obtains multiple data subsets by feature segmentation through sliding windows, and then inputs the obtained data subsets into the quantile random forest for cross-validation to obtain multiple class probability vectors for each sample, and finally splices the obtained class probability vectors with the original data set as new features to form a new data set, and uses the new combination of features to better represent the relationship between minority and majority classes, thus improve the overall classification performance of the classifier. Experiments on the KEEL dataset show that the improved algorithm improves the overall sample classification performance for unbalanced data compared to other algorithms.