Multi-Label Learning (MLL) solves the challenge of characterizing every sample via a particular feature which relates to the group of labels at once. That is, a sample has manifold views where every view is symbolized through a Class Label (CL). In the past decades, significant number of researches has been prepared towards this promising machine learning concept. Such researches on MLL have been motivated on a pre-determined group of CLs. In most of the appliances, the configuration is dynamic and novel views might appear in a Data Stream (DS). In this scenario, a MLL technique should able to identify and categorize the features with evolving fresh labels for maintaining a better predictive performance. For this purpose, several MLL techniques were introduced in the earlier decades. This article aims to present a survey on this field with consequence on conventional MLL techniques. Initially, various MLL techniques proposed by many researchers are studied. Then, a comparative analysis is carried out in terms of merits and demerits of those techniques to conclude the survey and recommend the future enhancements on MLL techniques.

Keywords : Multi-label learning, Label correlations, Multiple instances, Machine learning, Multi-label problem transformation.