Cautious classification with nested dichotomies and imprecise probabilities

• Published in: Soft computing (Berlin, Germany) Vol. 21; no. 24; pp. 7447 - 7462
• Main Authors: Yang, Gen, Destercke, Sébastien, Masson, Marie-Hélène
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2017; Springer Nature B.V; Springer Verlag
• Subjects: Artificial Intelligence; Classification; Computational Intelligence; Control; Costs; Decision making; Decomposition; Dichotomies; Engineering; Estimates; Image classification; Inference; Information retrieval; Learning; Machine Learning; Mathematical Logic and Foundations; Mechatronics; Methodologies and Application; Probability; Robotics; Statistics; Multi-class classification; Indeterminate prediction; Imprecise probabilities; Binary decomposition; Ordinal regression
• ISSN: 1432-7643; 1433-7479
• DOI: 10.1007/s00500-016-2287-7

In some applications of machine learning and information retrieval (e.g. medical diagnosis, image recognition, pre-classification...), it can be preferable to provide less informative but more reliable predictions. This can be done by making partial predictions in the form of class subsets when the available information is insufficient to provide a reliable unique class. Imprecise probabilistic approaches offer nice tools to learn models from which such cautious predictions can be produced. However, the learning and inference processes of such models are computationally harder than their precise counterparts. In this paper, we introduce and study a particular binary decomposition strategy, nested dichotomies, that offer computational advantages in both the learning (due to the binarization process) and the inference (due to the decomposition strategy) processes. We show with experiments that these computational advantages do not lower the performances of the classifiers, and can even improve them when the class space has some structure.