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ABClass : Une approche d'apprentissage multi-instances pour les séquences

Abstract : In Multiple Instance Learning (MIL) problem for sequence data, the learning data consist of a set of bags where each bag contains a set of instances/sequences. In some real world applications such as bioinformatics comparing a random couple of sequences makes no sense. In fact, each instance of each bag may have structural and/or functional relationship with other instances in other bags. Thus, the classification task should take into account the relation between semantically related instances across bags. In this paper, we present ABClass, a novel MIL approach for sequence data classification. Each sequence is represented by one vector of attributes extracted from the set of related instances. For each sequence of the unknown bag, a discriminative classifier is applied in order to compute a partial classification result. Then, an aggregation method is applied in order to generate the final result. We applied ABClass to solve the problem of bacterial Ionizing Radiation Resistance (IRR) prediction. The experimental results were satisfactory.
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Submitted on : Wednesday, July 11, 2018 - 1:36:58 PM
Last modification on : Wednesday, February 2, 2022 - 3:57:43 PM
Long-term archiving on: : Saturday, October 13, 2018 - 1:45:38 AM


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  • HAL Id : hal-01835432, version 1


Manel Zoghlami, Sabeur Aridhi, Mondher Maddouri, Engelbert Mephu Nguifo. ABClass : Une approche d'apprentissage multi-instances pour les séquences. RJCIA 2018 - 16èmes Rencontres des Jeunes Chercheurs en Intelligence Artificielle, Jul 2018, Nancy, France. pp.1-9. ⟨hal-01835432⟩



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