[BibTeX] [RIS]
Extensions of the Separate-and-Conquer Multilabel Rule Learner
Type of publication: Mastersthesis
Citation: ba:salem
Type: Bachelor Thesis
Year: 2017
Month: August
School: TU Darmstadt, Knowledge Engineering Group
URL: https://www.ke.tu-darmstadt.de/lehre/arbeiten/bachelor/2017/Salem_Borhan-Youssef.pdf
Abstract: 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 MencĂ­a 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.
Userfields: betreuer={ELM}
Authors Salem, Borhan Youssef