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

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.

Answer 1

[Don Mathis]

Look at your model at the MATLAB command line. If 'ClassNames' has only one entry, then it was 1-class training. For example:

SVMModel = 
  ClassificationSVM
             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'

[Francesco Formaggio]

Thank you for the answer. But then if I have outliers in the training, that is most points of class 1 with only some of class -1, how can I tell fitcsvm() to use still one-class SVM for training?

[Don Mathis]

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.