Towards Effective Classification of Imbalanced Data with Convolutional Neural Networks

Class imbalance in machine learning is a problem often found with real-world data, where data from one class clearly dominates the dataset. Most neural network classifiers fail to learn to classify such datasets correctly if class-to-class separability is poor due to a strong bias towards the majority class. In this paper we present an algorithmic solution, integrating different methods into a novel approach using a class-to-class separability score, to increase performance on poorly separable, imbalanced datasets using Cost Sensitive Neural Networks. We compare different cost functions and methods that can be used for training Convolutional Neural Networks on a highly imbalanced dataset of multi-channel time series data. Results show that, despite being imbalanced and poorly separable, performance metrics such as G-Mean as high as 92.8% could be reached by using cost sensitive Convolutional Neural Networks to detect patterns and correctly classify time series from 3 different datasets.