Nature-inspired Preference Learning Algorithms Using the Choquet Integral
[ 1 ] Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | [ 2 ] Instytut Informatyki, Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | [ SzD ] doktorant ze Szkoły Doktorskiej | [ P ] pracownik
2024
rozdział w monografii naukowej / referat
angielski
- Preference learning
- Choquet integral
- Evolutionary algorithm
- Particle swarm optimization
- Fish school search
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.
14.07.2024
440 - 448
20
140