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

R. Pighetti; Denis Pallez; Frédéric Precioso

doi:10.1109/SSCI.2015.197

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

R. Pighetti Denis Pallez ¹ Frédéric Precioso ²

1 I3S - Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis

2 Laboratoire d'Informatique, Signaux, et Systèmes de Sophia-Antipolis (I3S) / Equipe KEIA

Laboratoire I3S - SPARKS - Scalable and Pervasive softwARe and Knowledge Systems

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.

Keywords : Training data Training Buildings support vector machine multiobjective genetic algorithm Context Evolutionary computation genetic algorithms image classification support vector machines LSH MOGA SVM training sample selection content-based image classification fine-grained classification locality sensitive hashing multiobjective evolutionary algorithm

Document type :

Conference papers

Domain :

Computer Science [cs] / Operations Research [cs.RO]

Computer Science [cs] / Machine Learning [cs.LG]

Complete list of metadatas

https://hal.univ-cotedazur.fr/hal-01322765
Contributor : Denis Pallez <>
Submitted on : Friday, May 27, 2016 - 4:17:16 PM
Last modification on : Monday, November 5, 2018 - 3:52:10 PM

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

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