Mo`ath  Al-luwaici, Ahmad  Kadri Junoh, Farzana  Kabir Ahmad, 
    Investigating the Applicability of Several Fuzzy-Based Classifiers on
Multi-Label Classification,
    Journal of Engineering and Applied Sciences,
    Volume 14,Issue 19,
    2019,
    Pages 7210-7217,
    ISSN 1816-949x,
    jeasci.2019.7210.7217,
    (https://makhillpublications.co/view-article.php?doi=jeasci.2019.7210.7217)
    Abstract: 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.
    Keywords: Classification;fuzzy-logic;fuzzy-based classifiers;machine learning;multi-label classification;datasets