JCP 2014 Vol.9(3): 557-565 ISSN: 1796-203X
doi: 10.4304/jcp.9.3.557-565
doi: 10.4304/jcp.9.3.557-565
A Taxonomy of Label Ranking Algorithms
Yangming Zhou1, 2, Yangguang Liu1, Jiangang Yang1, Xiaoqi He1, Liangliang Liu1
1Ningbo Institute of Technology, Zhejiang University, Ningbo, 315100, Zhejiang Province, China
2Department of Control Science and Engineering, Zhejiang University, Hangzhou, 310027, Zhejiang Province, China
Abstract—The problem of learning label rankings is receiving increasing attention from machine learning and data mining community. Its goal is to learn a mapping from instances to rankings over a finite number of labels. In this paper, we devote to giving an overview of the state-of-the-art in the area of label ranking, and providing a basic taxonomy of the label ranking algorithms. Specifically, we classify these label ranking algorithms into four categories, namely decomposition methods, probabilistic methods, similaritybased methods, and other methods. We pay particular attention to the latest advances in each. Also, we discuss their strengths and weaknesses, and highlight some interesting challenges that remain to be solved.
Index Terms—label ranking, classification, multilabel learning, rank correlation
2Department of Control Science and Engineering, Zhejiang University, Hangzhou, 310027, Zhejiang Province, China
Abstract—The problem of learning label rankings is receiving increasing attention from machine learning and data mining community. Its goal is to learn a mapping from instances to rankings over a finite number of labels. In this paper, we devote to giving an overview of the state-of-the-art in the area of label ranking, and providing a basic taxonomy of the label ranking algorithms. Specifically, we classify these label ranking algorithms into four categories, namely decomposition methods, probabilistic methods, similaritybased methods, and other methods. We pay particular attention to the latest advances in each. Also, we discuss their strengths and weaknesses, and highlight some interesting challenges that remain to be solved.
Index Terms—label ranking, classification, multilabel learning, rank correlation
Cite: Yangming Zhou, Yangguang Liu, Jiangang Yang, Xiaoqi He, Liangliang Liu, "A Taxonomy of Label Ranking Algorithms," Journal of Computers vol. 9, no. 3, pp. 557-565, 2014.
General Information
ISSN: 1796-203X
Abbreviated Title: J.Comput.
Frequency: Bimonthly
Abbreviated Title: J.Comput.
Frequency: Bimonthly
Editor-in-Chief: Prof. Liansheng Tan
Executive Editor: Ms. Nina Lee
Abstracting/ Indexing: DBLP, EBSCO, ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat,etc
E-mail: jcp@iap.org
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