Introducing DeepBalance: Random Deep Belief Network Ensembles to Address Class Imbalance

When solving practical classification problems, a practitioner may be faced with class imbalance, meaning that one class has a significantly higher prevalence than the others (also called the majority class). Examples of imbalanced classification problems in the literature include [1], [2], [3], [4]. Class imbalance problems may be exacerbated in the future as we discover new methods to collect rare data and rate of data collection increases. In many class imbalance problems, the minority class is not only the interest, but also carries the higher misclassification cost, which complicates learning [5]. Machine learning classifiers try to find an optimal decision boundary that fits training data. As classifiers generally seek to find the simplest rule that partitions the training data, the simplest rule in imbalanced settings is often always predicting the majority class [6]. Results can be deceptive for such classifiers, as they may achieve high accuracy. For example, in a problem where a minority class occurs 0.1% of the time, an uninformed classifier can achieve 99.9% accuracy by simply always predicting observations as the majority. Thus, the naturally occurring target class distribution is not optimal for learning in highly imbalanced scenarios [7], [8], [9], [10].