How to force fitcsvm() to train a one-class svm?

27 views (last 30 days)
With fitcsvm you can train also a one-class svm, but how can I be sure that the trained svm is one-class and not two-class? I tried with
SVMModel = fitcsvm(valuesTraining,targetsTraining,'Standardize',true,... 'KernelFunction','gaussian','OptimizeHyperparameters','auto');
where targetsTraining is a vector of all 1s (since I have only samples coming from one class) amd valuesTraining is the matrix containing the 5-dimensional-features training points. Then I test the model with different data but whith instances of both classes. Performance are bad and I have the feeling that the training has been conducted with two-class svm, and not one-class.

Accepted Answer

Don Mathis
Don Mathis on 5 Oct 2018
Edited: Don Mathis on 5 Oct 2018
Look at your model at the MATLAB command line. If 'ClassNames' has only one entry, then it was 1-class training. For example:
SVMModel =
ResponseName: 'Y'
CategoricalPredictors: []
ClassNames: 1
ScoreTransform: 'none'
NumObservations: 150
Alpha: [77×1 double]
Bias: -15.977601047115476
KernelParameters: [1×1 struct]
Mu: [5.843333333333319 3.057333333333326]
Sigma: [0.828066127977862 0.435866284936697]
BoxConstraints: [150×1 double]
ConvergenceInfo: [1×1 struct]
IsSupportVector: [150×1 logical]
Solver: 'SMO'
Don Mathis
Don Mathis on 10 Oct 2018
If your Response variable has only 1 class, then 1-class training is used. If there are 2 classes, 2-class is used. If you have outliers that are already labelled -1, then you can use 2-class learning to find outliers.

Sign in to comment.

More Answers (0)

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!