Interpretable optimisation-based approach for hyper-box classification

Data classification is considered a fundamental research subject within the machine learning community. Researchers seek the improvement of machine learning algorithms in not only accuracy, but also interpretability. Interpretable algorithms allow humans to easily understand the decisions that a machine learning model makes, which is challenging for black box models. Mathematical programming-based classification algorithms have attracted considerable attention due to their ability to effectively compete with leading-edge algorithms in terms of both accuracy and interpretability. Meanwhile, the training of a hyper-box classifier can be mathematically formulated as a Mixed Integer Linear Programming (MILP) model and the predictions combine accuracy and interpretability. In this work, an optimisation-based approach is proposed for multi-class data classification using a hyper-box representation, thus facilitating the extraction of compact IF-THEN rules. The key novelty of our approach lies in the minimisation of the number and length of the generated rules for enhanced interpretability. Through a number of real-world datasets, it is demonstrated that the algorithm exhibits favorable performance when compared to well-known alternatives in terms of prediction accuracy and rule set simplicity.