@article{MAKHILLIJSC201611421350,
    title = {Improving Classification Performance by Using Feature Selection with Resampling},
    journal = {International Journal of Soft Computing},
    volume = {11},
    number = {4},
    pages = {255-269},
    year = {2016},
    issn = {1816-9503},
    doi = {ijscomp.2016.255.269},
    url = {https://makhillpublications.co/view-article.php?issn=1816-9503&doi=ijscomp.2016.255.269},
    author = {Raya,Sherihan and},
    keywords = {Feature selection,classification,resampling,NBNET,BFTREE},
    abstract = {Feature selection methods tend to identify the most relevant features for classification and can be
categorized as either subset selection (wrapper) methods or ranking (filter) methods. The main purpose of this
stusy is to prove that a feature selection preprocessing step could enhance classifiers performance by
eliminating redundant features. The proposed method consists of three stages; the first refines sample space
domain by resample filtering, the second minimizes feature space by applying subset evaluation algorithm and
the third measures the goodness of the resulting set of features using different classifiers. Tow experiments
carried out on the data sets from UCI repository. The proposed method is evaluated by measuring the accuracy,
number of selected features, precision, recall, f-measure, ROC area, time to build model, error rate and relative
absolute error. Tests are done on two main types of classifiers Naïve Bayes and its variance NBTREE, NBNET
and J48 with other tree classifiers Random Forest, BFTREE.}
    }