Elements of information theory
Elements of information theory
The nature of statistical learning theory
The nature of statistical learning theory
Making large-scale support vector machine learning practical
Advances in kernel methods
Feature selection in SVM text categorization
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
A comparison of neural network input vector selection techniques
IEA/AIE'2004 Proceedings of the 17th international conference on Innovations in applied artificial intelligence
Effective feature selection scheme using mutual information
Neurocomputing
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Feature selection is a well-researched problem, which can improve the network performance and speed up the training of the network. In this paper, we proposed an effective feature selection scheme for SVM using the hybrid of enhanced genetic algorithm and mutual information, in which mutual information between each input and each output of the data set is employed in mutation in evolutionary process to purposefully guide search direction based on some criterions. In order to avoid the noise fitness evaluation, in evaluating the fitness of an input subset, a SVM should adaptively adjust its parameters to obtain the best performance of network, so an enhanced GA is used to simultaneously evolve the input features and the parameters of SVM. By examining two real financial time series, the simulation of three different methods of feature selection shows that the feature selection using the hybrid of GA and MI can reduce the dimensionality of inputs, speed up the training of the network and get better performance.