NDLI: A Greedy Search Algorithm for Resolving the Lowermost C Threshold in SVM Classification

Content Provider	IEEE Xplore Digital Library
Author	Huichuan Duan Naiwen Liu
Copyright Year	2013
Description	Author affiliation: Sch. of Inf. Sci. & Eng., Shandong Normal Univ., Jinan, China (Huichuan Duan; Naiwen Liu)
Abstract	In this paper, the authors present a greedy search algorithm that solves the SVM classification (SVC) problem at the lowermost C end. By combining the SVC asymptotic behavior with empirical results, it can be sure that at sufficiently small cost, a threshold C0, all the minority samples becomes support vectors each with Lagrange multiplier C0, and equal number of majority samples will become support vectors whose Lagrange multipliers are also C0. With this evidence, SVC is transformed into a C-independent combinatorial optimization problem. Taking all the minority inputs as initial support vectors, a greedy algorithm is devised to choose majority class support vectors one by one each with minimal increase to the objective function in its turn. The greedy nature of the algorithm enables finding out the majority support vectors that near or at the majority margin. By taking the found majority support vectors initially and applying the algorithm to the minority class conversely, the support vectors that near the decision boundary are also resolved. Applying linear least squares fitting to both the majority margin and decision boundary, C0 is obtained as a function of kernel parameters. Experimental results show that the proposed algorithm can find C0 almost perfectly.
Sponsorship	Leshan Normal Univ.
Starting Page	190
Ending Page	193
File Size	571801
Page Count	4
File Format	PDF
e-ISBN	9781479925490
DOI	10.1109/CIS.2013.47
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Greedy algorithms Fitting Static VAr compensators Benchmark testing SVM classification lowermost C threshold greedy search Kernel linear least squares Optimization
Content Type	Text
Resource Type	Article

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