Improving SVM Training Sample Selection Using Multi-Objective Evolutionary Algorithm and LSH

Abstract : In this paper, we propose a new framework hybridizing a Support Vector Machine (SVM), a Multi-Objective Genetic Algorithm (MOGA) and a Locality Sensitive Hashing (LSH). The goal is to tackle fine-grained classification challenges which means classifying many classes with high similarities between classes and poor similarities inside one class. SVM is used for its ability of learning multi-class problem from very few training data. MOGA is used for optimizing training samples used by SVM so as to improve its learning rate. As data define a discrete set of instances and not a continuous solution space, LSH is used for mapping "optimal solutions" obtained by MOGA onto the closest real instances contained in the dataset. We evaluate our method for content-based image classification on the standard image database Caltech256 (i.e. 30000 images distributed in 256 classes). Experiments shows that our method outperforms reference approaches.
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Computational Intelligence, 2015 IEEE Symposium Series on, 2015, Cape Town, South Africa. pp.1383-1390, 2015, Computational Intelligence and Data Mining. <10.1109/SSCI.2015.197>
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Contributeur : Denis Pallez <>
Soumis le : vendredi 27 mai 2016 - 16:17:16
Dernière modification le : samedi 28 mai 2016 - 01:13:45

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R. Pighetti, Denis Pallez, Frédéric Precioso. Improving SVM Training Sample Selection Using Multi-Objective Evolutionary Algorithm and LSH. Computational Intelligence, 2015 IEEE Symposium Series on, 2015, Cape Town, South Africa. pp.1383-1390, 2015, Computational Intelligence and Data Mining. <10.1109/SSCI.2015.197>. <hal-01322765>

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