Abstract
A new classification algorithm based on support vector machine and Rough set theory is proposed in the paper. We make great use of the advantages of Rough set theory in dealing with vagueness and uncertainty information, firstly select important features by attribute reduction; secondly select effective samples by rule induction; finally construct support vector classifier by the selected important features and effective samples. Thus it can reduce training samples’ dimensions, decrease training samples’ scales and noise disturbing. It can provide us with the benefits of improving support vector machine’s training speed and classification accuracy. Result of image recognition verifies its efficiency and feasibility. It also provides us an effective method to deal with the large scale and high dimensions data set.
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Xu, Y., Zhen, L., Yang, L., Wang, L. (2009). Classification Algorithm Based on Feature Selection and Samples Selection. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5552. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01510-6_71
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DOI: https://doi.org/10.1007/978-3-642-01510-6_71
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01509-0
Online ISBN: 978-3-642-01510-6
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