A Literature Survey on Algorithms for Multi-label Learning

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning (2017, background): “Because these attributes are not mutually exclusive (especially for images containing multiple individuals), this task is a multilabel classification (43, 44) problem.”
Thou shalt not hate: Countering Online Hate Speech (2018, methodology): “For evaluation purpose, we report the strict metric exact match ratio (Sorower, 2010).”
Investigating Capsule Networks with Dynamic Routing for Text Classification (2018, methodology): “Following (Sorower, 2010), we adopt Micro Averaged Precision (Precision), Micro Averaged Recall (Recall) and Micro Averaged F1 scores (F1) as the evaluation metrics for multi-label text classiﬁcation.”