Liapis, GI; Papageorgiou, LG; (2023) Hyper-box Classification Model Using Mathematical Programming. In: Sellmann, Meinolf and Tierney, Kevin, (eds.) LION 2023: Learning and Intelligent Optimization. (pp. pp. 16-30). Springer: Cham, Switzerland.

Preview

Text LNCS_paper.pdf - Other Download (651kB) | Preview

Abstract

Classification constitutes focal topic of study within the machine learning research community. Interpretable machine learning algorithms have been gaining ground against black box models because people want to understand the decision-making process. Mathematical programming based classifiers have received attention because they can compete with state-of-the-art algorithms in terms of accuracy and interpretability. This work introduces a single-level hyper-box classification approach, which is formulated mathematically as Mixed Integer Linear Programming model. Its objective is to identify the patterns of the dataset using a hyper-box representation. Hyper-boxes enclose as many samples of the corresponding class as possible. At the same time, they are not allowed to overlap with hyper-boxes of different class. The interpretability of the approach stems from the fact that IF-THEN rules can easily be generated. Towards the evaluation of the performance of the proposed method, its prediction accuracy is compared to other state-of-the-art interpretable approaches in a number of real-world datasets. The results provide evidence that the algorithm can compare favourably against well-known counterparts.

Download activity - last month

Export as CSVXMLJSON

Download activity - last 12 months

Export as XMLCSVJSON

Downloads by country - last 12 months

Export as JSONCSVXML

Archive Staff Only

View Item