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A Rank Aggregation Algorithm for Ensemble of Multiple Feature Selection Techniques in Credit Risk Evaluation
2016
International Journal of Advanced Research in Artificial Intelligence (IJARAI)
In credit risk evaluation the accuracy of a classifier is very significant for classifying the high-risk loan applicants correctly. Feature selection is one way of improving the accuracy of a classifier. It provides the classifier with important and relevant features for model development. This study uses the ensemble of multiple feature ranking techniques for feature selection of credit data. It uses five individual rank based feature selection methods. It proposes a novel rank aggregation
doi:10.14569/ijarai.2016.050901
fatcat:lgykg57nfbe2bh7djvibflx7iq