Authors :
S.K.Komagal Yallini; Dr. B.Mukunthan
Volume/Issue :
Volume 5 - 2020, Issue 7 - July
Google Scholar :
http://bitly.ws/9nMw
Scribd :
https://bit.ly/30CfC3g
DOI :
10.38124/IJISRT20JUL198
Abstract :
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.
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.
