Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Making large-scale support vector machine learning practical
Advances in kernel methods
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
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
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A model for a complex polynomial SVM kernel
SMO'08 Proceedings of the 8th conference on Simulation, modelling and optimization
Evaluation of a hybrid method for constructing multiple SVM kernels
ICCOMP'09 Proceedings of the WSEAES 13th international conference on Computers
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Fusions of GA and SVM for anomaly detection in intrusion detection system
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
Multi-objective parameters selection for SVM classification using NSGA-II
ICDM'06 Proceedings of the 6th Industrial Conference on Data Mining conference on Advances in Data Mining: applications in Medicine, Web Mining, Marketing, Image and Signal Mining
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The problem of determining optimal decision model is a difficult combinatorial task in the fields of pattern classification and machine learning. In this paper, we propose a new method to find the optimal decision model for SVM, which consists of the minimal set of highly discriminative features and the set of parameters for the kernel. To cope with this problem, we adopted genetic algorithm (GA) which provides efficient optimization tool simulating the natural evolution procedures in iterative fashion to select the optimal set of features and set of kernel parameters. In the method, the decision models generated by GA are evaluated by SVM, and GA selects the only good models and gives the selected models the chance to survive and improve by crossover and mutation operation. Combining GA and SVM, we can obtain the optimal decision model which reduces the execution time as well as improves the classification rate of SVM. We also demonstrated the feasibility of our proposed method by several experiments on the sets of clinical data such as KDD Cup 1999 intrusion detection pattern samples and stomach cancer proteome pattern samples.