The nature of statistical learning theory
The nature of statistical learning theory
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
Asymptotic behaviors of support vector machines with Gaussian kernel
Neural Computation
Supervised fuzzy clustering for the identification of fuzzy classifiers
Pattern Recognition Letters
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Breast cancer diagnosis using least square support vector machine
Digital Signal Processing
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Gene Classification Using Codon Usage and Support Vector Machines
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Using support vector machine with a hybrid feature selection method to the stock trend prediction
Expert Systems with Applications: An International Journal
Evolving least squares support vector machines for stock market trend mining
IEEE Transactions on Evolutionary Computation - Special issue on computational finance and economics
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
Dimensionality reduction using genetic algorithms
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Information Technology in Biomedicine
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Image Processing
Ultrasonic liver tissue characterization by feature fusion
Expert Systems with Applications: An International Journal
Association rule-based feature selection method for Alzheimer's disease diagnosis
Expert Systems with Applications: An International Journal
Applied Soft Computing
Modeling observer stress for typical real environments
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
Support vector machines (SVM) are an emerging data classification technique with many diverse applications. The feature subset selection, along with the parameter setting in the SVM training procedure significantly influences the classification accuracy. In this paper, the asymptotic behaviors of support vector machines are fused with genetic algorithm (GA) and the feature chromosomes are generated, which thereby directs the search of genetic algorithm to the straight line of optimal generalization error in the superparameter space. On this basis, a new approach based on genetic algorithm with feature chromosomes, termed GA with feature chromosomes, is proposed to simultaneously optimize the feature subset and the parameters for SVM. To evaluate the proposed approach, the experiment adopts several real world datasets from the UCI database and from the Benchmark database. Compared with the GA without feature chromosomes, the grid search, and other approaches, the proposed approach not only has higher classification accuracy and smaller feature subsets, but also has fewer processing time.