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DOI: 10.14569/IJACSA.2019.0101264
PDF

Multi-Label Classification using an Ontology

Author 1: Yaya TRAORE
Author 2: Sadouanouan MALO
Author 3: Didier BASSOLE
Author 4: Abdoulaye SERE

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 10 Issue 12, 2019.

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Abstract: During these last few years, the problem of multi-label classification (ML) has been studied in several domains, such as text categorization. Multi-label classification is a main challenging task because each instance can be assigned to multiple classes simultaneously. This paper studies the problem of Multi-label classification in the context of web pages categorization. The categories are defined in an ontology. Among the weakness of the multi-label classification methods, exist the number of positive and negative examples used to build the training dataset of a specific label. So the challenge comes from the huge number of labels combinations that grows exponentially. In this paper, we present an ontology-based Multi-label classification which exploit dependence between the labels. In addition, our approach uses the ontology to take into account relationships between labels and to give the selection of positive and negative examples in the learning phase. In the prediction phase, if a label is not predicted, the ontology is used to prune the set of descendant labels. The results of experimental evaluation show the effectiveness of our approaches.

Keywords: Multi-label classification (ML); Binary Relevance (BR); ontology; categorization; prediction

Yaya TRAORE, Sadouanouan MALO, Didier BASSOLE and Abdoulaye SERE, “Multi-Label Classification using an Ontology” International Journal of Advanced Computer Science and Applications(IJACSA), 10(12), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0101264

@article{TRAORE2019,
title = {Multi-Label Classification using an Ontology},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0101264},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0101264},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {12},
author = {Yaya TRAORE and Sadouanouan MALO and Didier BASSOLE and Abdoulaye SERE}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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