Abstract
An effective feature selection scheme is proposed, which utilizes the combination of wrapper and filter: ant colony optimization (ACO) and mutual information (MI). By examining the modeling based on SVMs 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.
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Zhang, C., Hu, H. (2005). Ant Colony Optimization Combining with Mutual Information for Feature Selection in Support Vector Machines. In: Zhang, S., Jarvis, R. (eds) AI 2005: Advances in Artificial Intelligence. AI 2005. Lecture Notes in Computer Science(), vol 3809. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11589990_110
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DOI: https://doi.org/10.1007/11589990_110
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30462-3
Online ISBN: 978-3-540-31652-7
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