A New Multi-class Support Vector Machine with Multi-sphere in the Feature Space

Hao, Pei-Yi; Lin, Yen-Hsiu

doi:10.1007/978-3-540-73325-6_75

Cited by 25 publications

(25 citation statements)

References 6 publications

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“…In order to apply one-class SVM for multiclass problem, multiple models of one-class SVM will be trained together. Hao and Lin [11] formulated multiple one-class SVM models as one large optimization problem to minimize the total errors, while Refs. [12,13] used separate one-class SVM models and iteratively adjusted the threshold of each models to maximize the total accuracy.…”

Section: One-class Svmmentioning

confidence: 99%

Robust multiclass classification for learning from imbalanced biomedical data

Phoungphol

Zhang

Zhao

2012

Tinshhua Sci. Technol.

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Imbalanced data is a common and serious problem in many biomedical classification tasks. It causes a bias on the training of classifiers and results in lower accuracy of minority classes prediction. This problem has attracted a lot of research interests in the past decade. Unfortunately, most research efforts only concentrate on 2-class problems. In this paper, we study a new method of formulating a multiclass Support Vector Machine (SVM) problem for imbalanced biomedical data to improve the classification performance. The proposed method applies cost-sensitive approach and ramp loss function to the Crammer and Singer multiclass SVM formulation. Experimental results on multiple biomedical datasets show that the proposed solution can effectively cure the problem when the datasets are noisy and highly imbalanced.

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Section: One-class Svmmentioning

confidence: 99%

Robust multiclass classification for learning from imbalanced biomedical data

Phoungphol

Zhang

Zhao

2012

Tinshhua Sci. Technol.

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“…SVDD has parameters that control the margin error. Two approaches based on SVDD were proposed in [8,9]. The concept behind these approaches is to determine one SVDD for each class of the training set and tunes its parameter to improve the accuracy of each classifier.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, these approaches resemble more the optimal Bayesian classifier where the optimal decision is slightly deviated to the class with the smaller variance. The method proposed in [8] trains each SVDD separately by solving an optimization problem while the one proposed in [9] trains all the SVDD by formulating one optimization problem.…”

Section: Introductionmentioning

confidence: 99%

A multiclass decision rule minimizing a loss function based on one class SVM

Jrad

Grall‐Maës

Beauseroy

2008

2008 IEEE Workshop on Machine Learning for Signal Processing

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A multiclass algorithm based on ν-1-SVM which minimizes a loss function is introduced. The loss function allows to use decision costs which depend on the classes, and to consider partial and total rejection. The algorithm is based on deriving the ν-1 SVM regularization path for each class. The decision rule is determined by tuning all the ν-1-SVM parameters and all the other decision parameters together in order to minimize the loss function. Experimental results on artificial data sets and some benchmark data sets are provided to assess the effectiveness of this approach.

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“…Therefore, one should infer a classifier from a more or less limited set of training examples. In the classical decision framework, referred as the classical framework, many historical strands of research can be identified: statistical, Support Vector Machines, Neural Network Bishop (2006); Guobin & Lu (2007); Hao & Lin (2007) ;Husband & Lin (2002); Vapnik (1998) ;Yang et al (2007)... In the class-selective rejection scheme, fewer works have been done Ha (1997); Horiuchi (1998).…”

Section: Introductionmentioning

confidence: 99%

“…Taking advantage of the regularization path method, the entire parameters searching space is considered. Compared to similar approaches Bottou et al (1994); Hao & Lin (2007); Yang et al (2007), since the searching space is widely extended, the selected decision rule is more likely to be the optimal one. Note that standard multiclass learning strategy is a particular case of the proposed approaches where the different decision options are given by the pre-defined classes, the loss function is given by the error rate and no constraint is considered.…”

Section: Introductionmentioning

confidence: 99%