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Graph-Based Safe Support Vector Machine for Multiple Classes
- Source :
- IEEE Access, Vol 6, Pp 28097-28107 (2018)
- Publication Year :
- 2018
- Publisher :
- IEEE, 2018.
-
Abstract
- Semi-supervised learning (SSL) utilizes limited labeled data and plenty of unlabeled data, and it has attracted attentions for its improved learning performance. However, recent studies have indicated that using unlabeled data, in some cases, could deteriorate the performance. Therefore, there's an imminent need to develop safe semi-supervised learning methods to determine whether SSL should be applied for a given scenario. This paper proposes a safe version of multi-class graph-based semi-supervised support vector machine (SVM). At first, in order to eliminate the impact of bad label assignments, a criterion based on the cost function of semi-supervised SVM (S3VM) is introduced to evaluate the predicted label assignments.Then, m candidate optimal label assignments are picked up by the criterion. After that, a multi-class safe strategy is designed to generate the final label assignment whose performance is never worse than that of the methods using only labeled samples. Experimental results on several benchmark datasets validate the effectiveness of the proposed technique Refereed/Peer-reviewed
- Subjects :
- semi-supervised learning
General Computer Science
Computer science
0102 computer and information sciences
02 engineering and technology
Machine learning
computer.software_genre
01 natural sciences
Kernel (linear algebra)
0202 electrical engineering, electronic engineering, information engineering
General Materials Science
safe strategy
business.industry
Graph based
Supervised learning
General Engineering
multi-class SVM
Support vector machine
ComputingMethodologies_PATTERNRECOGNITION
graph-based SVM
010201 computation theory & mathematics
Semi-supervised learning
Task analysis
Graph (abstract data type)
Labeled data
020201 artificial intelligence & image processing
Artificial intelligence
lcsh:Electrical engineering. Electronics. Nuclear engineering
business
computer
lcsh:TK1-9971
Subjects
Details
- Language :
- English
- ISSN :
- 21693536
- Volume :
- 6
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....d506b0e0053a89c63bd0433d853a8068