@article{MAKHILLJEAS2019141918483,
    title = {Investigating the Applicability of Several Fuzzy-Based Classifiers on
Multi-Label Classification},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {14},
    number = {19},
    pages = {7210-7217},
    year = {2019},
    issn = {1816-949x},
    doi = {jeasci.2019.7210.7217},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2019.7210.7217},
    author = {Mo`ath,Ahmad and},
    keywords = {Classification,fuzzy-logic,fuzzy-based classifiers,machine learning,multi-label classification,datasets},
    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.}
    }