=Paper=
{{Paper
|id=Vol-1088/invited1
|storemode=property
|title=Exploiting Label Relationship in Multi-Label Learning
|pdfUrl=https://ceur-ws.org/Vol-1088/invited1.pdf
|volume=Vol-1088
|dblpUrl=https://dblp.org/rec/conf/ijcai/Zhou13
}}
==Exploiting Label Relationship in Multi-Label Learning==
<pdf width="1500px">https://ceur-ws.org/Vol-1088/invited1.pdf</pdf>
<pre>
   Exploiting Label Relationship in Multi-Label
                    Learning
                                Zhi-Hua Zhou

      National Key Laboratory for Novel Software Technology,
                    Nanjing University, China
                                zhouzh@nju.edu.cn




                                   Abstract
In many real data mining tasks, one data object is often associated with multi-
ple class labels simultaneously; for example, a document may belong to multiple
topics, an image can be tagged with multiple terms, etc. Multi-label learning
focuses on such problems, and it is well accepted that the exploitation of rela-
tionship among labels is crucial; actually this is the essential difference between
multi-label learning and conventional (single-label) supervised learning.
    Most multi-label learning approaches try to capture label relationship and
then apply it to help construct prediction models. Some approaches rely on ex-
ternal knowledge resources such as label hierarchies, and some approaches try to
exploit label relationship by counting the label co-occurrences in training data.
These approaches are effective in many cases; however, in real practice, the ex-
ternal label relationship is often unavailable, and generating label relationship
from training data and then applying to the same training data for model con-
struction will greatly increase the overfitting risk. Moreover, the label relation-
ship is usually assumed symmetric, and almost all existing approaches exploit
it globally by assuming the label correlation be shared among all instances.




                                   Short Bio

Zhi-Hua Zhou is a professor at Nanjing University. His research interests are
mainly in machine learning, data mining, pattern recognition and multimedia
information retrieval. In these areas he has published more than 100 papers
in leading international journals or conferences, and holds 12 patents. He is
the recipient of the IEEE CIS Outstanding Early Career Award, the Fok Ying
Tung Young Professorship Award, the Microsoft Young Professorship Award,
the National Science & Technology Award for Young Scholars of China, and
many other awards including nine international journal/conference paper or
competition awards. He is an Associate Editor-in-Chief of ”Chinese Science
Bulletin”, Associate Editor or Editorial Boards member of ”ACM Trans. Intel-
ligent Systems and Technology” and twelve other journals. He is the Founder
and Steering Committee Chair of ACML, and Steering Committee member of
PAKDD and PRICAI. He is the Chair of the AI&PR Technical Committee
of the China Computer Federation, Chair of the Machine Learning Technical
Committee of the China Association of AI, the Vice Chair of the Data Mining
Technical Committee of the IEEE Computational Intelligence Society, and the
Chair of the IEEE Computer Society Nanjing Chapter. He is a Fellow of the
IAPR, Fellow of the IEEE, and Fellow of the IET/IEE.

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