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The Journal of Supercomputing

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01-08-2016

Fast kernel feature ranking using class separability for big data mining

Author: Zhiliang Liu

Published in: The Journal of Supercomputing | Issue 8/2016

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Abstract

Kernel feature ranking often delivers many benefits for big data mining, e.g., improving generalization performance. However, its efficiency is quite challenging due to a need of tuning kernel parameters in the ranking process. In present work, we propose a computational-light metric based on kernel class separability for kernel feature ranking. In the proposed metric, the kernel parameter is optimized by a proposed analytical algorithm rather than an optimization search algorithm. Experimental results demonstrate that (1) the proposed metric can lead to a fast and robust kernel feature ranking; and (2) the proposed analytical algorithm can select a right kernel parameter with much less computation time for two state-of-the-arts kernel metrics.

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Title: Fast kernel feature ranking using class separability for big data mining
Author: Zhiliang Liu
Publication date: 01-08-2016
Publisher: Springer US
Published in: The Journal of Supercomputing / Issue 8/2016
Print ISSN: 0920-8542
Electronic ISSN: 1573-0484
DOI: https://doi.org/10.1007/s11227-015-1481-1

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