@inproceedings{SisLab1939,
       booktitle = {International Conference on Nature of Computation and Communication},
           month = {October},
           title = {FRFE: Fast Recursive Feature Elimination for Credit Scoring},
          author = {Van Sang Ha and Ha Nam Nguyen},
            year = {2016},
           pages = {133--142},
             url = {https://eprints.uet.vnu.edu.vn/eprints/id/eprint/1939/},
        abstract = {Abstract Credit scoring is one of the most important issues in financial decision-making. The use of data mining techniques to build models for credit scoring has been a hot topic in recent years. Classification problems often have a large number of features, but not all of them are useful for classification. Irrelevant and redundant features in credit data may even reduce the classification accuracy. Feature selection is a process of selecting a subset of relevant features, which can decrease the dimensionality, reduce the running time, and  ...}
}