@article{MAKHILLAJIT201615226503,
    title = {Rough Set Based Approach for Multiclass Breast Tissue Classification},
    journal = {Asian Journal of Information Technology},
    volume = {15},
    number = {22},
    pages = {4438-4444},
    year = {2016},
    issn = {1682-3915},
    doi = {ajit.2016.4438.4444},
    url = {https://makhillpublications.co/view-article.php?issn=1682-3915&doi=ajit.2016.4438.4444},
    author = {V.P.,K. and},
    keywords = {Breast tissue classification,rough set,multiclass classification,rule induction},
    abstract = {Breast cancer is the most common diseases among women and the detection of breast cancer for
younger women at an earlier stage can save lives. To identify breast cancer most of the existing work is based
on mammography images and has achieved higher accuracy. But the usual mammography test is not suitable
and it is not recommended for younger women. Hence, the Electrical Impedance Spectrum (EIS) test for breast
cancer detection is more suitable for younger women, the proposed work make use of EIS data to detect breast
cancer. The proposed research is based on rough set due to lot of uncertainty in breast tissue data set. The
result of classification accuracy and error rate is evaluated. This result indicates that the proposed rough set
based approach achieves average accuracy of 65% in six class problem, 80% in four class problem and 85% in
two class problem. The two decision classes acquired >80% accuracy using fivefold validation.}
    }