الفهرس 
Research GoalsOnly 14 pages are availabe for public view
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Abstract
Label dependencies are the biggest influencing factor on performance
multi-label classification, directly and indirectly. A point, which clearly
distinguishes multi-label from multi-class problems, is the possible
dependencies between the classes. Hence, the key challenge in multilabel
learning is how to exploit this dependency effectively, because both
empirical and theoretical studies have proved that it increases the
performance of the learning process. Usually label correlations are given
in advance. However, in some problem domains unfortunately this
knowledge is unavailable, therefore new techniques are required to obtain
the correlations between labels. We’ll try to exploit these correlations in
this research. This is to be reached by discovering the correlation between
classes using association rule, and then using a divide and conquer
technique, the problem is divided into multiple smaller problems each
sub-problem containing related labels is solved separately and then the
result is integrated forming a global solution for the original problem.
The thesis comprises seven chapters, these are organized as follows:
In chapter two, we introduce concepts, notations and corresponding
basic formal definitions required throughout this work. Furthermore, it
discusses and review relevant existing and newly introduced evaluation
measures, and provides an in depth study of multi-label data
In chapter three, presents the three categories of methods for multi-label
learning and discusses the advantages and disadvantages of each method,
and then evaluate multi-label methods.