A note on genetic algorithms for large-scale feature selection
Pattern Recognition Letters
Elements of information theory
Elements of information theory
Axiomatic Approach to Feature Subset Selection Based on Relevance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Input Feature Selection by Mutual Information Based on Parzen Window
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Subset Selection Using a Genetic Algorithm
IEEE Intelligent Systems
Hybrid Genetic Algorithms for Feature Selection
IEEE Transactions on Pattern Analysis and Machine 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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In the artificial neural networks (ANNs), feature selection is a well-researched problem, which can improve the network performance and speed up the training of the network. The statistical-based methods and the artificial intelligence-based methods have been widely used to feature selection, and the latter are more attractive. In this paper, using genetic algorithm (GA) combining with mutual information (MI) to evolve a nearoptimal input feature subset for ANNs is proposed, 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. By examining the forecasting at the Australian Bureau of Meteorology, the simulation of three different methods of feature selection shows that the proposed method can reduce the dimensionality of inputs, speed up the training of the network and get better performance.