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2017 | OriginalPaper | Buchkapitel

Kernel Construction and Feature Subset Selection in Support Vector Machines

verfasst von : Shinichi Yamada, Kourosh Neshatian

Erschienen in: Simulated Evolution and Learning

Verlag: Springer International Publishing

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Abstract

Kernel functions have an important role in the performance of Support Vector Machines (SVMs), since they form the geometry of the feature space. Manual designing of kernel functions is an expensive task and requires domain-specific knowledge. In this article, we propose a new method to automatically construct kernel functions and select optimal subsets of features. We achieve this by combining primitive kernels and subsets of features using Genetic Programming (GP). Our experiments show that the proposed method drastically improves the prediction accuracy of SVMs.

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Vorheriges Kapitel A Hybrid GA-GP Method for Feature Reduction in Classification

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Titel: Kernel Construction and Feature Subset Selection in Support Vector Machines
verfasst von: Shinichi Yamada
Kourosh Neshatian
Verlag: Springer International Publishing
Buch: Simulated Evolution and Learning
Print ISBN: 978-3-319-68758-2

Electronic ISBN: 978-3-319-68759-9

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-68759-9_49

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