An Adaptation of Binary Relevance for Multi-Label Classification applied to Functional Genomics

Erica Akemi TanakaJosé Augusto Baranauskas

Many classification problems, especially in the field of bioinformatics are associated with more than one class, known as multi-label classification problems. In this study we propose a new adaptation for the Binary Relevance method taking into account the correlation among labels, focusing on the interpretability of the model, not only its performance. The experimental results shown that our proposal has a performance comparable to other methods as the same time it provides an interpretable model from the multi-label problem.

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