Hi Elvira,
I understand that you are looking for a way to assign weights to specific features of your dataset when using the 'fitcecoc' function. However, it is worth noting that the 'fitcecoc' function in MATLAB does not provide a direct mechanism to assign weights to individual features. Its primary purpose is to train error-correcting output code (ECOC) multiclass models using binary base learners.
The ‘weights’ property in fitcecoc weighs the observations in each row of X with the corresponding value in weights, where X is predictor data. So, it is not possible to pass weights for feature vectors(columns) in this function.
If you want to emphasize certain features in your SVM classifier, one common approach is feature selection or feature engineering. Here are a few options you can consider:
- Univariate Feature Selection: Use statistical tests like chi-square, ANOVA, or mutual information to evaluate the relevance of features with respect to the target variable. Select the top-ranked features based on their scores.
- L1 Regularization: SVM models with L1 regularization (L1-norm SVM) tend to produce sparse solutions by setting less relevant feature weights to zero. You can tune the regularization parameter to control the sparsity and emphasize key features.
You can also look up these documentation links of some of the feature selections methods available in MATLAB.
By applying one or more of these techniques, you can select or engineer features that have a more significant impact on your SVM classifier's performance.
