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I am looking for a multiclass classifier that can handle repeated measures. Specifically, each of my subjects appears multiple times with the same number of n classes. Now I would like to fit a classifier to this dataset to get an intuition for the feature-label relationship (that is, which features are the most associated with which classes?). Is there a multiclass classifier that can take this into account?

EDIT:

My problem is a multi-class problem, not a multi-label problem. I did not know that 'multilabel' and 'multiclass' describe different problems, so I changed the wording.

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  • $\begingroup$ All your data points have the same labels? I can see both subj_id 1 and 2 having the labels 1, 2 and 3. Or I’m missing something? $\endgroup$
    – rivu
    Commented Oct 21, 2021 at 14:23
  • $\begingroup$ Yes, that's correct. I am interested in the feature label relationship while accounting for possible within-subject correlations. $\endgroup$ Commented Oct 21, 2021 at 14:32
  • $\begingroup$ If every point has the same class labels [1, 2, 3], this is not a multi label classification problem. For a feature f, every value it can take produces a class label l. In other words, the class label is not an RV. So I don’t know if a correlation can even be computed. $\endgroup$
    – rivu
    Commented Oct 21, 2021 at 14:44
  • $\begingroup$ Imagine training the classifier only using the features and the y label and further imagine you wouldn't know the subject id. Then someone comes and gives you this additional information. Then I guess one could argue that feature sets within each subjects are more similar to each other than they are between subjects? In other words, that there is a nested structure in the feature set? $\endgroup$ Commented Oct 21, 2021 at 15:06
  • $\begingroup$ Do all your subjects have the same labels in the same order? e.g. subject_1_labels = [1, 2, 3], then subject_2_labels = [1, 2, 3], subject_n_labels = [1, 2, 3]. Then I don't think there will be any predictive method informative here since the labels are all the same across subject and also have the same ordering as well. $\endgroup$
    – Kirk Walla
    Commented Oct 22, 2021 at 15:25

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This might not be a complete answer but as a first step I would just fit a decision tree and then plot the feature importance to get a better understanding regarding the relationship between $X$ and $Y$. For more info regarding weather your case is a multi-label or a multi-task setting you can look here and decide accordingly as to how to proceed.

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  • $\begingroup$ Sorry, I just realized that I mistakenly used the term 'multilabel' instead of 'multiclass'. Made an edit to my question. $\endgroup$ Commented Nov 9, 2021 at 18:13

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