@article{MAKHILLIJSC201813621455,
    title = {Extracting Knowledge from Incomplete Dataset by Tolerance Rough Sets with
Consistency Measure},
    journal = {International Journal of Soft Computing},
    volume = {13},
    number = {6},
    pages = {149-157},
    year = {2018},
    issn = {1816-9503},
    doi = {ijscomp.2018.149.157},
    url = {https://makhillpublications.co/view-article.php?issn=1816-9503&doi=ijscomp.2018.149.157},
    author = {Elsayed and},
    keywords = {Knowledge extraction,similarity relation,tolerance rough set,step-deck coordination function,consistency measure,classification accuracy},
    abstract = {Knowledge extraction from various datasets is suffering from missing attribute values, representing
the non-categorical ones, besides the inconsistency among the local rules. These shorts reflect two main
problems, information granules representation and superfluous feature existence. This study treats these
problems based on tolerance rough sets. A similarity relation is considered based on the matching percentage
among objects in order to decrease the knowledge granularity and hence, increase the information capacity.
In order to measure the dependencies among features, a tolerance measure based on step-deck coordination
function is defined as vague inclusion function. For feature extraction process, a consistency function is
defined. It is proved that the consistency measure promise an increase in classification accuracy. More
specifically, the proposed model receives a base method of tolerance rough set to generate local rules that
describe the application algorithm. The details and limitations of the proposed model are discussed where a
comparative study is appeared.}
    }