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
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Proceedings of the ACM International Conference on Image and Video Retrieval
Human-oriented image retrieval of optimized multi-feature via genetic algorithm
ICICA'10 Proceedings of the First international conference on Information computing and applications
The colour and texture - a novel image retrieval technology based on human vision
International Journal of Innovative Computing and Applications
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Content-based image retrieval (CBIR) is an important and widely studied topic since it can have significant impact on multimedia information retrieval. Recently, support vector machine (SVM) has been applied to the problem of CBIR. The SVM-based method has been compared with other methods such as neural network (NN) and logistic regression, and has shown good results. Genetic algorithm (GA) has been increasingly applied in conjunction with other AI techniques. However, few studies have dealt with the combining GA and SVM, though there is a great potential for useful applications in this area. This paper focuses on simultaneously optimizing the parameters and feature subset selection for SVM without degrading the SVM classification accuracy by combining GA for CBIR. In this study, we show that the proposed approach outperforms the image classification problem for CBIR. Compared with NN and pure SVM, the proposed approach significantly improves the classification accuracy and has fewer input features for SVM.