Extraction and optimization of classification ...
Type de document :
Compte-rendu et recension critique d'ouvrage
Titre :
Extraction and optimization of classification rules for temporal sequences: Application to hospital data
Auteur(s) :
Vandromme, Maxence [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Alicante [Seclin]
Jacques, Julie [Auteur]
Alicante [Seclin]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Taillard, Julien [Auteur]
Alicante [Seclin]
Arnaud, Hansske [Auteur]
Groupement des Hôpitaux de l'Institut Catholique de Lille [GHICL]
Jourdan, Laetitia [Auteur]
Operational Research, Knowledge And Data [ORKAD]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Université de Lille
Dhaenens, Clarisse [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Université de Lille, Sciences et Technologies
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Alicante [Seclin]
Jacques, Julie [Auteur]
Alicante [Seclin]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Taillard, Julien [Auteur]
Alicante [Seclin]
Arnaud, Hansske [Auteur]
Groupement des Hôpitaux de l'Institut Catholique de Lille [GHICL]
Jourdan, Laetitia [Auteur]
![refId](/themes/Mirage2//images/idref.png)
Operational Research, Knowledge And Data [ORKAD]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Université de Lille
Dhaenens, Clarisse [Auteur]
![refId](/themes/Mirage2//images/idref.png)
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Université de Lille, Sciences et Technologies
Titre de la revue :
Knowledge-Based Systems
Pagination :
148-158
Éditeur :
Elsevier
Date de publication :
2017-05-28
ISSN :
0950-7051
Mot(s)-clé(s) en anglais :
Data mining
Classification
Temporal data
Optimization
Classification
Temporal data
Optimization
Discipline(s) HAL :
Informatique [cs]/Intelligence artificielle [cs.AI]
Computer Science [cs]/Operations Research [math.OC]
Computer Science [cs]/Operations Research [math.OC]
Résumé en anglais : [en]
This study focuses on the problem of supervised classification on heterogeneous temporal data featuring a mixture of attribute types (numeric, binary, symbolic, temporal). We present a model for classification rules designed ...
Lire la suite >This study focuses on the problem of supervised classification on heterogeneous temporal data featuring a mixture of attribute types (numeric, binary, symbolic, temporal). We present a model for classification rules designed to use both non-temporal attributes and sequences of temporal events as predicates. We also propose an efficient local search-based metaheuristic algorithm to mine such rules in large scale, real-life data sets extracted from a hospital’s information system. The proposed algorithm, MOSC (Multi-Objective Sequence Classifier), is compared to standard classifiers and previous works on these real data sets and exhibits noticeably better classification performance. While designed with medical applications in mind, the proposed approach is generic and can be used for problems from other application domains.Lire moins >
Lire la suite >This study focuses on the problem of supervised classification on heterogeneous temporal data featuring a mixture of attribute types (numeric, binary, symbolic, temporal). We present a model for classification rules designed to use both non-temporal attributes and sequences of temporal events as predicates. We also propose an efficient local search-based metaheuristic algorithm to mine such rules in large scale, real-life data sets extracted from a hospital’s information system. The proposed algorithm, MOSC (Multi-Objective Sequence Classifier), is compared to standard classifiers and previous works on these real data sets and exhibits noticeably better classification performance. While designed with medical applications in mind, the proposed approach is generic and can be used for problems from other application domains.Lire moins >
Langue :
Anglais
Vulgarisation :
Non
Collections :
Source :