@article{MAKHILLJEAS202015719237,
    title = {An Intelligent Classifier for Group Decision Making Based on Rough Sets},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {15},
    number = {7},
    pages = {1805-1808},
    year = {2020},
    issn = {1816-949x},
    doi = {jeasci.2020.1805.1808},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2020.1805.1808},
    author = {E.I. and},
    keywords = {Rough set,decision making,multiple classifiers,quality of decision accuracy},
    abstract = {In this study, a new approach to combine multiple decision systems using multiple classifiers and
rough sets methods is presented. This approach depends on our proposed algorithms that work to combine
multiple decision systems by aggregating the lower and upper approximations. This improves the quality of
decision rules by increasing the number of certain rules which enable us to make certain decisions. Our
experiment results indicate that combining lower and upper approximations improves the quality of decision
rules. Furthermore, it increases the classification accuracy computed by single and multiple classifiers
compared to existing methods.}
    }