Abstract
In consideration of the problem that different features have different contributions to recognition results in pattern recognition for composite plates bonding flaws, this paper proposes feature weighting SVM algorithm based on information gain. The algorithm firstly calculates the contributions of all features to recognition results by means of information gain, then weights kernel function in recognition and decision making via the results combining with Support Vector Machines in order to emphasize the roles of important features in the process of recognition and eliminate or alleviate the influence of wake-relevant and irrelevant features on recognition results. Both theory analysis and experience show that this algorithm can be used to recognize bonding flaw of composite plate accurately and quantitatively.
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Zhang, Z., Wang, Xf., Wang, Ht., Liu, Yx. (2012). Research on Quantitative Recognition for Composite Plate Bonding Flaw Based on Feature Weighting SVM. In: Zhao, M., Sha, J. (eds) Communications and Information Processing. Communications in Computer and Information Science, vol 288. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31965-5_42
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DOI: https://doi.org/10.1007/978-3-642-31965-5_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31964-8
Online ISBN: 978-3-642-31965-5
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