Extraction and optimization of classification rules for temporal sequences: Application to hospital data

Maxence Vandromme 1, 2 Julie Jacques 2, 3 Julien Taillard 2 Hansske Arnaud 4 Laetitia Jourdan 5, 6, 7 Clarisse Dhaenens 1, 8
1 DOLPHIN - Parallel Cooperative Multi-criteria Optimization
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
2 Alicante [Seclin]
3 LIFL - Laboratoire d'Informatique Fondamentale de Lille
4 GHICL
5 ORKAD - Operational Research, Knowledge And Data
CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
6 CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
7 Université de Lille
8 Université de Lille, Sciences et Technologies
Abstract : 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.
Keywords : Data mining Classification Temporal data Optimization
Type de document :
Article dans une revue
Knowledge-Based Systems, Elsevier, 2017, 122, pp.148-158. 〈10.1016/j.knosys.2017.02.001〉
Liste complète des métadonnées
https://hal.archives-ouvertes.fr/hal-01564520
Contributeur : Laetitia Jourdan <>
Soumis le : mardi 18 juillet 2017 - 18:16:18
Dernière modification le : jeudi 21 février 2019 - 10:52:46

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INSTITUT-TELECOM | CRISTAL | UNIV-LILLE3 | INRIA2017 | INRIA | CRISTAL-ORKAD | UNIV-LILLE

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Maxence Vandromme, Julie Jacques, Julien Taillard, Hansske Arnaud, Laetitia Jourdan, et al.. Extraction and optimization of classification rules for temporal sequences: Application to hospital data. Knowledge-Based Systems, Elsevier, 2017, 122, pp.148-158. 〈10.1016/j.knosys.2017.02.001〉. 〈hal-01564520〉

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