Interpretable multi-label classification by means of multivariate linear regression

Bierman, Surette (2019)

CITATION: Bierman, S. 2019. Interpretable multi-label classification by means of multivariate linear regression. South African Statistics Journal, 53(1):1-13.

The original publication is available at https://journals.co.za

Article

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.

Please refer to this item in SUNScholar by using the following persistent URL: http://hdl.handle.net/10019.1/106261
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