Abstract
In this paper we propose a dependence measure for a pair of features. This measure aims at identifying redundant features where the relationship between the features is characterized by higher degree polynomials. An algorithm is also proposed to make effective use of this dependence measure for the feature selection. Neither the calculation of dependence measure, nor the algorithm need the class values of the observations. So they can be used for clustering as well as classification.
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Keywords
- Feature Selection
- Dependence Measure
- Feature Subset
- Feature Selection Method
- Feature Selection Algorithm
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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© 2009 Springer-Verlag Berlin Heidelberg
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Jain, N., Murthy, C.A. (2009). Feature Selection Using Non Linear Feature Relation Index. In: Chaudhury, S., Mitra, S., Murthy, C.A., Sastry, P.S., Pal, S.K. (eds) Pattern Recognition and Machine Intelligence. PReMI 2009. Lecture Notes in Computer Science, vol 5909. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11164-8_2
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DOI: https://doi.org/10.1007/978-3-642-11164-8_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-11163-1
Online ISBN: 978-3-642-11164-8
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