Title :

A hybrid CP/MOLS approach for multi-objective imbalanced classification

Author(s) :

Szczepanski, Nicolas [Auteur]
Operational Research, Knowledge And Data [ORKAD]
Audemard, Gilles [Auteur]
Centre de Recherche en Informatique de Lens [CRIL]
Jourdan, Laetitia [Auteur]
Operational Research, Knowledge And Data [ORKAD]
Lecoutre, Christophe [Auteur]
Centre de Recherche en Informatique de Lens [CRIL]
Mousin, Lucien [Auteur]
Université Catholique de Lille - Faculté de gestion, économie et sciences [UCL FGES]
Operational Research, Knowledge And Data [ORKAD]
Veerapen, Nadarajen [Auteur]
Operational Research, Knowledge And Data [ORKAD]

Conference title :

GECCO '21: Genetic and Evolutionary Computation Conference

City :

Lille

Country :

France

Start date of the conference :

2021-07-10

Journal title :

GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference

Publisher :

ACM

Publication date :

2021-06

HAL domain(s) :

Computer Science [cs]/Operations Research [math.OC]
Informatique [cs]/Intelligence artificielle [cs.AI]

English abstract : [en]

In the domain of partial classification, recent studies about multiobjective local search (MOLS) have led to new algorithms offering high performance, particularly when the data are imbalanced. In the presence of such data, ...
Show more >In the domain of partial classification, recent studies about multiobjective local search (MOLS) have led to new algorithms offering high performance, particularly when the data are imbalanced. In the presence of such data, the class distribution is highly skewed and the user is often interested in the least frequent class. Making further improvements certainly requires exploiting complementary solving techniques (notably, for the rule mining problem). As Constraint Programming (CP) has been shown to be effective on various combinatorial problems, it is one such promising complementary approach. In this paper, we propose a new hybrid combination, based on MOLS and CP that are quite orthogonal. Indeed, CP is a complete approach based on powerful filtering techniques whereas MOLS is an incomplete approach based on Pareto dominance. Experimental results on real imbalanced datasets show that our hybrid approach is statistically more efficient than a simple MOLS algorithm on both training and tests instances, in particular, on partial classification problems containing many attributes.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL