Högskolan i Skövde

Many real-world machine learning classification problems suffer from imbalanced training data, where the least frequent label has high relevance and significance for the end user, such as equipment breakdowns or various types of process anomalies. This imbalance can negatively impact the learning algorithm and lead to misclassification of minority labels, resulting in erroneous actions and potentially high unexpected costs. Most previous oversampling methods rely only on the minority samples, often ignoring their overall density and distribution in relation to the other classes. In addition, most of them lack in the oversampling method’s explainability. In contrast, this paper proposes a novel oversampling method that considers a subspace of the feature-set for the creation of synthetic minority samples using nonlinear optimization of a class-sensitive objective function. Suitable subspaces for oversampling are identified through a Bayesian reinforcement strategy based on Dirichlet smoothing, which may be useful for explainable-AI. An empirical comparison of the proposed method is performed with 10 existing techniques on 18 real-world datasets using two traditional machine learning classifiers and four evaluation metrics. Statistical analysis of cross-validated runs over the 18 datasets and four metrics (i.e. 72 experiments) reveals that the proposed approach is among the best performing methods in 6 and 2 instances when using random forest classifier and support vector machine classifier, thus placing it at the top. The study also reveals that some feature combinations are more important than others for minority oversampling, and the proposed approach offers a way to identify such features.