A Survey and Current Research Challenges in Multi-Label Classification Methods
Purvi Prajapati1, Amit Thakkar2, Amit Ganatra3
1Purvi Prajapati, Department of Information Technology, Charotar University of Science and Technology Changa, Anand, Gujarat, India.
2Amit Thakkar, Department of Information Technology, Charotar University of Science and Technology Changa, Anand, Gujarat, India.
3Amit Ganatra, U and P U Patel Department of Computer Engineering, , Charotar University of Science and Technology Changa, Anand, Gujarat, India.
Manuscript received on February 15, 2012. | Revised Manuscript received on February 20, 2012. | Manuscript published on March 05, 2012. | PP: 238-258 | Volume-2 Issue-1, March 2012. | Retrieval Number: A0427022112 /2012©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Classification is used to predict class of unseen instance as accurate as possible. Multi label classification is a variant of single label classification where set of labels associated with single instance. Multi label classification is used by modern applications, such as text classification, functional genomics, image classification, music categorization etc. This paper introduces the task of multi-label classification, methods for multi-label classification and evolution measure for multi-label classification. Also done comparative analysis of multi label classification methods on the basis of theoretical study and than on the basis of simulation done on various data sets.
Keywords: Classification, Single label problem, Multi label problem