Cooperative Co-Evolution for Ensembles of Nested Dichotomies for Multi-Class Classification

Wever, Marcel ; Özdogan, Miran and Hüllermeier, Eyke (2023): Cooperative Co-Evolution for Ensembles of Nested Dichotomies for Multi-Class Classification. Genetic and Evolutionary Computation Conference (GECCO '23), Lisbon, Portugal, July 15-19, 2023. In: Proceedings of the Genetic and Evolutionary Computation Conference, New York, NY, USA: Association for Computing Machinery. 597–605

Abstract

In multi-class classification, it can be beneficial to decompose a learning problem into several simpler problems. One such reduction technique is the use of so-called nested dichotomies, which recursively bisect the set of possible classes such that the resulting subsets can be arranged in the form of a binary tree, where each split defines a binary classification problem. Recently, a genetic algorithm for optimizing the structure of such nested dichotomies has achieved state-of-the-art results. Motivated by its success, we propose to extend this approach using a co-evolutionary scheme to optimize both the structure of nested dichotomies and their composition into ensembles through which they are evaluated. Furthermore, we present an experimental study showing this approach to yield ensembles of nested dichotomies at substantially lower cost and, in some cases, even with an improved generalization performance.

• Item Type: Conference or Workshop Item (Paper)
• Keywords: coevolution, nested dichotomy, supervised learning
• 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
• Place of Publication: New York, NY, USA
• Language: English