Support Vector Machine Classifier With Pinball Loss

Traditionally, the hinge loss is used to construct support vector machine (SVM) classifiers. The hinge loss is related to the shortest distance between sets and the corresponding classifier is hence sensitive to noise and unstable for re-sampling. In contrast, the pinball loss is related to the quan...

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Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence Vol. 36; no. 5; pp. 984 - 997
Main Authors: Xiaolin Huang, Lei Shi, Suykens, Johan A. K.
Format: Journal Article
Language:English
Published: Los Alamitos, CA IEEE 01.05.2014
IEEE Computer Society
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithmics. Computability. Computer arithmetics
Algorithms
Applied sciences
Classification
Classifier design and evaluation
Classifiers
Computer science; control theory; systems
Data processing. List processing. Character string processing
Exact sciences and technology
Fasteners
Hinges
Kernel
Loss measurement
Memory organisation. Data processing
Noise
Optimization
Quantiles
Regression
Robustness
Software
Support vector machines
Theoretical computing
pinball loss
support vector machine
Classification
Function evaluation
Data analysis
Stability
Vector support machine
Noise immunity
Sampling
Computational complexity
Quantile
Signal to noise ratio
ISSN:0162-8828, 1939-3539, 2160-9292, 1939-3539
Online Access:Get full text
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Summary:Traditionally, the hinge loss is used to construct support vector machine (SVM) classifiers. The hinge loss is related to the shortest distance between sets and the corresponding classifier is hence sensitive to noise and unstable for re-sampling. In contrast, the pinball loss is related to the quantile distance and the result is less sensitive. The pinball loss has been deeply studied and widely applied in regression but it has not been used for classification. In this paper, we propose a SVM classifier with the pinball loss, called pin-SVM, and investigate its properties, including noise insensitivity, robustness, and misclassification error. Besides, insensitive zone is applied to the pin-SVM for a sparse model. Compared to the SVM with the hinge loss, the proposed pin-SVM has the same computational complexity and enjoys noise insensitivity and re-sampling stability.
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ISSN:0162-8828
1939-3539
2160-9292
1939-3539
DOI:10.1109/TPAMI.2013.178