Title
Nature-inspired Preference Learning Algorithms Using the Choquet Integral
Authors
[ 1 ] Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | [ 2 ] Instytut Informatyki, Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | [ SzD ] doctoral school student | [ P ] employee
Scientific discipline (Law 2.0)
Year of publication
2024
Chapter type
chapter in monograph / paper
Publication language
english
Keywords
EN
- Preference learning
- Choquet integral
- Evolutionary algorithm
- Particle swarm optimization
- Fish school search
Abstract
EN We introduce various algorithms for learning the parameters of a threshold-based sorting procedure powered by the Choquet integral. This model accounts for interactions between monotonic criteria and facilitates categorizing decision alternatives into predefined, preferentially ordered classes. We focus on developing heuristic preference learning methods capable of efficiently processing large datasets of classification examples. Specifically, we utilize Local Search, Simulated Annealing, and nature-inspired approaches such as Genetic Algorithm, Fish School Search, and Particle Swarm Optimization. We demonstrate the effectiveness of the proposed model through a case study. Additionally, we present an experimental comparison of the recommendation accuracy achieved by these algorithms on a suite of benchmark sorting problems.
Date of online publication
14.07.2024
Pages (from - to)
440 - 448
Ministry points / chapter
20
Ministry points / conference (CORE)
140
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