TY  - JOUR
T1  - Investigating the Applicability of Several Fuzzy-Based Classifiers on
Multi-Label Classification
AU - Al-luwaici, Mo`ath AU - Kadri Junoh, Ahmad AU - Kabir Ahmad, Farzana 
JO  - Journal of Engineering and Applied Sciences
VL  - 14
IS  - 19
SP  - 7210
EP  - 7217
PY  - 2019
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2019.7210.7217
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2019.7210.7217
KW  - Classification
KW  -fuzzy-logic
KW  -fuzzy-based classifiers
KW  -machine learning
KW  -multi-label classification
KW  -datasets
AB  - In the last few decades, fuzzy logic has been extensively used in several domains such as economy,
decision making, logic and classification. In specific, fuzzy logic which is a powerful mathematical
representation has shown a superior performance with uncertainty real-life applications comparing with other
learning approaches. Many researchers utilized the concept of fuzzy logic in solving the traditional single label
classification problems of both types: binary classification and multi-class classification. Unfortunately, very
few researches have utilized fuzzy logic in a more general type of classification that is called Multi-Label
Classification (MLC). Hence, this study aims to examine the applicability of fuzzy logic to be used with MLC
through evaluating several fuzzy-based classifiers on five different multi-label datasets. The results revealed
that the utilizing fuzzy-based classifiers on solving the problem of MLC is promising comparing with a wide
range of MLC algorithms that belong to several learning approaches and strategies.
ER  -