| Various multi-label classification algorithms are broadly developed in the literature. Each of these existing methods has different strengths and drawbacks, and very few attempts have been performed to address the uncertainty. Recently, neutral zone classifiers have been presented to deal with ambiguous data to improve the accuracy of the estimated classification results by trading off the relatively high penalty of an incorrect classification with a lower penalty for remaining neutral about class membership. The existing neutral zone methods was developed to construct multiple single-label classifiers for multi-label data. In this work, we present different classification alternatives for multi-label data using a standard logistic regression model, a generalized linear mixed model and Markov random field to relax the current underlying assumptions. The proposed neutral zone classification methods are implemented and tested on simulation data sets and on the biological data set. Their results suggest that our proposed classification approaches are useful alternatives for practical application when working with multi-label data. |
