A GA-Based Feature Subset Selection and Parameter Optimization of Support Vector Machine for Content --- Based Image Retrieval

Authors:
Kwang-Kyu Seo
Affiliations:
Department of Industrial Information and Systems Engineering, Sangmyung University, San 98-20, Anso-Dong, Chonan, Chungnam 330-720, Korea
Venue:
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
Year:
2007

Citing 9
Cited 2

Content-Based Image Retrieval at the End of the Early Years

IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
Feature Subset Selection Using a Genetic Algorithm

IEEE Intelligent Systems
Feature Selection via Concave Minimization and Support Vector Machines

ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Feature Selection for Support Vector Machines by Means of Genetic Algorithms

ICTAI '03 Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence
The Evolving Tree—A Novel Self-Organizing Network for Data Analysis

Neural Processing Letters
Expert system based on artificial neural networks for content-based image retrieval

Expert Systems with Applications: An International Journal
Dimensionality reduction using genetic algorithms

IEEE Transactions on Evolutionary Computation
Feature subset selection for support vector machines through discriminative function pruning analysis

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

A simultaneous feature adaptation and feature selection method for content-based image retrieval systems

Knowledge-Based Systems
Feature subset selection using improved binary gravitational search algorithm

Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents the effectiveness of applying genetic algorithm (GA)-based feature subset selection and parameter optimization of support vector machine (SVM) for content-based image retrieval (CBIR). SVM, one of the new techniques for pattern classification, has been widely used in many application areas. The kernel parameters setting for SVM in the training process impacts on the classification accuracy. Feature subset selection is another factor that impacts classification accuracy. The objective of this study is to simultaneously optimize the parameters and feature subset without degrading the SVM classification accuracy using the GA-based approach for CBIR. In this study, we show that the proposed GA-based approach outperforms SVM to the problem of the image classification problem in CBIR. Compared with NN and SVM algorithm, the proposed GA-based approach significantly improves the classification accuracy and has fewer input features for SVM.