Strategies for Training Robust Neural Network Based Digit Recognizers on Unbalanced Data Sets

The performance of a neural network in a pattern recognition task may be influenced by several factors. One of these factors is related to the considerable difference between the number of examples belonging to each class to be recognized. The effect called imbalanced data can negatively influence the ability of a recognizer to learn the concept of the minority class. In this work we propose an under-sampling strategy based on selecting samples lying around the decision surface and an over-sampling strategy which uses kernel density estimation to populate the minority class. The experimental results on Roman and Bangla digit data using a neural network based recognizer confirm the effectiveness of the proposed solutions.