TY  - CONF
ID  - SisLab1939
UR  - http://link.springer.com/chapter/10.1007/978-3-319-46909-6_13
A1  - Ha, Van Sang
A1  - Nguyen, Ha Nam
Y1  - 2016/10//
N2  - 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  ...
TI  - FRFE: Fast Recursive Feature Elimination for Credit Scoring
SP  - 133
AV  - none
EP  - 142
T2  - International Conference on Nature of Computation and Communication
ER  -