NDLI: Fast optimizing parameters algorithm for least squares support vector machine based on artificial immune algorithm

Content Provider	IEEE Xplore Digital Library
Author	Fugang Yang
Copyright Year	2009
Description	Author affiliation: School of information & electronics engineering Shandong Institute of Business and Technology, Yan'tai, 264005 (Fugang Yang)
Abstract	When Least Squares Support Vector Machine (LS-SVM) is used to classify on large datasets, training samples to get the optimal model parameters is a time-consuming and memory consumption process. To reduce training time and computational complexity, we develop a novel algorithm for selecting LS-SVM meta-parameter values based on ideas from principle of artificial immune. By analyzing LS-SVM parameters on the classification accuracy, we find there are many parameters combinations that make the same classification accuracy; What's more, once one of the parameters fixed and the other changes in a certain range, their combinations do not affect the classification accuracy. We regard LS-SVM parameters as antibody genes and design reasonable coding scheme for them. Then we employ artificial immune algorithm to search the optimal model parameters of LS-SVM. We provide experiments to demonstrate the performance of LS-SVM. Results show that the proposed algorithm greatly enhances parameters optimizing efficiency while keeping the approximately same classification accuracy with the some other existent methods such as multi-fold cross-validation and grid-search.
File Size	103402
File Format	PDF
ISBN	9781424438631
DOI	10.1109/ICEMI.2009.5274372
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-08-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Least Squares Support Vector Machine Instruments Optimization methods Data engineering Electronic mail Artificial Immune Algorithm Computational complexity Least squares approximation Least squares methods Support vector machines Support vector machine classification Parameters Optimization Kernel
Content Type	Text
Resource Type	Article

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