Handling numerical data to evolve ...
Type de document :
Communication dans un congrès avec actes
Titre :
Handling numerical data to evolve classification rules using a Multi-Objective Local Search
Auteur(s) :
Vandromme, Maxence [Auteur correspondant]
Alicante [Seclin]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Jacques, Julie [Auteur]
Alicante [Seclin]
Taillard, Julien [Auteur]
Alicante [Seclin]
Dhaenens, Clarisse [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Jourdan, Laetitia [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Alicante [Seclin]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Jacques, Julie [Auteur]
Alicante [Seclin]
Taillard, Julien [Auteur]
Alicante [Seclin]
Dhaenens, Clarisse [Auteur]

Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Jourdan, Laetitia [Auteur]

Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Titre de la manifestation scientifique :
Metaheuristics International Conference (MIC)
Ville :
Agadir
Pays :
Maroc
Date de début de la manifestation scientifique :
2015-06-07
Date de publication :
2015
Mot(s)-clé(s) en anglais :
Classification
local search
numerical data
local search
numerical data
Discipline(s) HAL :
Computer Science [cs]/Operations Research [math.OC]
Informatique [cs]/Apprentissage [cs.LG]
Informatique [cs]/Apprentissage [cs.LG]
Résumé en anglais : [en]
Classification is a key problem in the machine learning field, and some metaheuristics have been successfully adapted to answer this problem. However, difficulties commonly arise when a classi- fication problem is described ...
Lire la suite >Classification is a key problem in the machine learning field, and some metaheuristics have been successfully adapted to answer this problem. However, difficulties commonly arise when a classi- fication problem is described by numerical attributes, which are very common in most real-world tasks. Therefore, a metaheuristic-based classification algorithm often needs to be adapted to support this new type of attributes. In this study, we propose a method for representing and evolving classifi- cation rules with numerical attributes and extend the MOCA-I classification algorithm to support this type of attributes. We investigate several variants for the neighborhood generation mechanism that is at the core of the local search process, and propose two improvements on the general algorithm. Experimentations are done to evaluate the performance of each of the proposed variants.Lire moins >
Lire la suite >Classification is a key problem in the machine learning field, and some metaheuristics have been successfully adapted to answer this problem. However, difficulties commonly arise when a classi- fication problem is described by numerical attributes, which are very common in most real-world tasks. Therefore, a metaheuristic-based classification algorithm often needs to be adapted to support this new type of attributes. In this study, we propose a method for representing and evolving classifi- cation rules with numerical attributes and extend the MOCA-I classification algorithm to support this type of attributes. We investigate several variants for the neighborhood generation mechanism that is at the core of the local search process, and propose two improvements on the general algorithm. Experimentations are done to evaluate the performance of each of the proposed variants.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :