TY  - RPRT
ID  - ba:salem
T1  - Extensions of the Separate-and-Conquer Multilabel Rule Learner
A1  - Salem, Borhan Youssef
Y1  - 2017
M1  - Bachelor Thesis
T2  - TU Darmstadt, Knowledge Engineering Group
UR  - /lehre/arbeiten/bachelor/2017/Salem_Borhan-Youssef.pdf
N2  - Multi-label classification is the task in Machine Learning to assign more than one label to an instance.
Opposite to the single-label classification problem, where only a binary or a multi-class can be assigned to
an instance, dependencies may exist between different labels in a multi-label problem. These dependencies
can be used to improve the classification task and help to better understanding the multi-label dataset. A
Separate-and-Conquer Multi-Label Rule Learner was proposed 2016 by Eneldo Loza Mencía and Frederik
Janssen, that learn multi-label dependency and use them in the classification task. In this work we made
some extensions of the proposed algorithm and evaluate them.
M1  - betreuer={ELM}
ER  -