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

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.
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Article dans une revue
Knowledge-Based Systems, Elsevier, 2017, 122, pp.148-158. 〈10.1016/j.knosys.2017.02.001〉
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https://hal.archives-ouvertes.fr/hal-01564520
Contributeur : Laetitia Jourdan <>
Soumis le : mardi 18 juillet 2017 - 18:16:18
Dernière modification le : mardi 3 juillet 2018 - 11:22:26

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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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