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HAL Université Côte d'Azur |  |
Conference papers
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.
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
https://hal.univ-cotedazur.fr/hal-01322765
Contributor : Denis Pallez <>
Submitted on : Friday, May 27, 2016 - 4:17:16 PM
Last modification on : Monday, October 12, 2020 - 10:30:35 AM
Contributor : Denis Pallez <>
Submitted on : Friday, May 27, 2016 - 4:17:16 PM
Last modification on : Monday, October 12, 2020 - 10:30:35 AM