Interpretable multi-label classification by means of multivariate linear regression - Bierman, Surette (South African Statistical Journal, 2019)
In this paper, the potential of using a multivariate regression approach in order to obtain interpretable output in a multi-label classification problem is investigated. We focus in our analysis on extensions of ordinary multivariate regression which take into account informative dependencies amongst labels. It is found that the regression approaches make a valuable contribution insofar as the importance of input variables for given labels can be evaluated. An empirical study facilitates comparison of the performance of the regression approaches in multi-label classification and, in terms of several evaluation measures, shows that they are also largely competitive with state-of-the-art multi-label classification procedures.