An unsupervised learning approach to resolving the data imbalanced issue in supervised learning problems in functional genomics

Abstract

Learning from imbalanced data occurs very frequently in functional genomic applications. One positive example to thousands of negative instances is common in scientific applications. Unfortunately, traditional machine learning treats the extremely small instances as noise. The standard approach for this difficulty is balancing training data by resampling them. However, this results in high false positive predictions. Hence, we propose preprocessing majority instances by partitioning them into clusters. This greatly reduces the ambiguity between minority instances and instances in each cluster. For moderately high imbalance ratio and low in-class complexity, our technique gives better prediction accuracy than undersampling method. For extreme imbalance ratio like splice site prediction problem, we demonstrate that this technique serves as a good filter with almost perfect recall that reduces the amount of imbalance so that traditional classification techniques can be deployed and yield significant improvements over previous predictor. We also show that the technique works for sub cellular localization and post-translational modification site prediction problems.