@article{MAKHILLJEAS2018132117074,
    title = {Improved Performance of Support Vector Machine for Imbalanced
Data Sets Using Oversampling and Optimization},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {13},
    number = {21},
    pages = {9065-9077},
    year = {2018},
    issn = {1816-949x},
    doi = {jeasci.2018.9065.9077},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2018.9065.9077},
    author = {Sana and},
    keywords = {Support vector machines,oversampling,optimization algorithm,noisy borderline imbalanced data
sets,real imbalanced data sets,proposed methodology},
    abstract = {Classification of imbalanced data sets particularly in the presence of noise is a significant problem
in machine learning and data mining. Support Vector Machine (SVM) is one of the most renowned supervised
classification algorithm. However, its performance becomes limited for imbalanced data sets. To improve the
performance of SVM for imbalanced data sets including noisy borderline and real data sets, a methodology
based on oversampling and optimization algorithm is proposed for two-class classification problems. By
generating the synthetic samples in the minority class and searching the best choices of the parameters of SVM
after minimizing the objective function, the performance of SVM is improved. To confirm the validity of the
proposed methodology, an experimental study including noisy borderline and real imbalanced data sets was
conducted. SVM was applied by using the proposed methodology, two optimization algorithms and one
oversampling algorithm on all the data sets. The performance of SVM with all methods was evaluated using
sensitivity, G mean and F-measure. A significantly improved performance of SVM was observed by using the
proposed methodology.}
    }