Abstract
Hyper Surface Classification (HSC), which is based on Jordan Curve Theorem in Topology, is one of the accurate and efficient classification algorithms. The hyper surface obtained by the training process exhibits excellent generalization performance on datasets not only of large size but also of high dimensionality. The classification knowledge hidden in the classifier, however, is hard to interpret by human. How to obtain the classification rules is an important problem. In this paper, we firstly extract rule from the sample directly. In order to avoid rule redundance, two optimal policies, selecting Minimal Consistent Subset (MCS) for the training set and merging some neighboring cubes, are exerted to reduce the rules set. Experimental results show that the two policies are able to accurately acquire the knowledge implied by the hyper surface and express the good generalization performance of HSC. Moreover, the time for classifying the unlabeled sample by the rules set can be shorten correspondingly.
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He, Q., Li, J., Shi, Z. (2009). Rule Extraction and Reduction for Hyper Surface Classification. 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_11
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DOI: https://doi.org/10.1007/978-3-642-01510-6_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01509-0
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