Set-valued prediction in hierarchical classification with constrained representation complexity

Mortier, Thomas ; Hüllermeier, Eyke ; Dembczyński, Krzysztof und Waegeman, Willem (August 2022): Set-valued prediction in hierarchical classification with constrained representation complexity. 38th Conference on Uncertainty in Artificial Intelligence, Eindhoven, Netherlands, 1-5 August 2022. Cussens, James und Zhang, Kun (eds.) : In: Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence, Vol. 180 PMLR. pp. 1392-1401

Abstract

Set-valued prediction is a well-known concept in multi-class classification. When a classifier is uncertain about the class label for a test instance, it can predict a set of classes instead of a single class. In this paper, we focus on hierarchical multi-class classification problems, where valid sets (typically) correspond to internal nodes of the hierarchy. We argue that this is a very strong restriction, and we propose a relaxation by introducing the notion of representation complexity for a predicted set. In combination with probabilistic classifiers, this leads to a challenging inference problem for which specific combinatorial optimization algorithms are needed. We propose three methods and evaluate them on benchmark datasets: a naïve approach that is based on matrix-vector multiplication, a reformulation as a knapsack problem with conflict graph, and a recursive tree search method. Experimental results demonstrate that the last method is computationally more efficient than the other two approaches, due to a hierarchical factorization of the conditional class distribution.

• Item Type: Conference or Workshop Item (Paper)
• Form of publication: Publisher's Version
• Faculties: Mathematics, Computer Science and Statistics > Computer Science > Artificial Intelligence and Machine Learning
• Subjects: 000 Computer science, information and general works > 000 Computer science, knowledge, and systems