Abstract
The learning of classification models to predict class labels of new and previously unseen data instances is one of the most essential tasks in data mining. A popular approach to classification is ensemble learning, where a combination of several diverse and independent classification models is used to predict class labels. Ensemble models are important as they tend to improve the average classification accuracy over any member of the ensemble. However, classification models are also often required to be explainable to reduce the risk of irreversible wrong classification. Explainability of classification models is needed in many critical applications such as stock market analysis, credit risk evaluation, intrusion detection, etc. Unfortunately, ensemble learning decreases the level of explainability of the classification, as the analyst would have to examine many decision models to gain insights about the causality of the prediction. The aim of the research presented in this paper is to create an ensemble method that is explainable in the sense that it presents the human analyst with a conditioned view of the most relevant model aspects involved in the prediction. To achieve this aim the authors developed a rule-based explainable ensemble classifier termed Ranked ensemble G-Rules (ReG-Rules) which gives the analyst an extract of the most relevant classification rules for each individual prediction. During the evaluation process ReG-Rules was evaluated in terms of its theoretical computational complexity, empirically on benchmark datasets and qualitatively with respect to the complexity and readability of the induced rule sets. The results show that ReG-Rules scales linearly, delivers a high accuracy and at the same time delivers a compact and manageable set of rules describing the predictions made.