NDLI: Fast Extended One-Versus-Rest Multi-Label Support Vector Machine Using Approximate Extreme Points

Content Provider	Directory of Open Access Journals (DOAJ)
Author	Zhongwei Sun Zhongwen Guo Chao Liu Xupeng Wang Jing Liu Shiyong Liu
Abstract	Existing extended one-versus-rest multi-label support vector machine (OVR-ESVM) adopting non-linear kernel is seriously restricted by excessive training time when it is applied to large-scale data set. In order to overcome this problem, we improve the OVR-ESVM by introducing the principle of approximate extreme points and new approximate ranking loss to construct a novel extended OVR-ESVM using approximate extreme points (AEML-ESVM). By optimizing only on the representative set which can be acquired via adopting the approximate extreme points method, the AEML-ESVM classification algorithm can substantially shorten the training time and its classification performance is comparable to that of the OVR-ESVM classification algorithm. And it uses the new approximate ranking loss as empirical loss term to exploit label correlation of individual instance directly. Experimental study on three benchmark large-scale data sets illustrates that AEML-ESVM classification algorithm can reduce training time greatly and achieve comparable classification performance with OVR-ESVM classification algorithm. And it is also superior to the existing fast multi-label SVM classification algorithms in terms of classification performance and training time.
e-ISSN	21693536
DOI	10.1109/ACCESS.2017.2699662
Journal	IEEE Access
Volume Number	5
Language	English
Publisher	IEEE
Publisher Date	2017-01-01
Publisher Place	United States
Access Restriction	Open
Subject Keyword	Electrical Engineering. Electronics. Nuclear Engineering Support Vector Machine Multi-label Classification Approximate Extreme Points Label Correlation Non-linear Kernel
Content Type	Text
Resource Type	Article

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