Rank Aggregation for Filter Feature Selection in Credit Scoring

The credit industry is a fast growing field, credit institutions collect data about credit customer and use them to build credit model. The collected information may be full of unwanted and redundant features which may speed down the learning process, so, effective feature selection methods are needed for credit dataset. In general, Filter feature selection methods outperform other feature selection techniques because they are effective and computationally fast. Choosing the appropriate filtering method from the wide variety of classical filtering methods proposed in the literature is a crucial issue in machine learning. So, we propose a feature selection fusion model that fuses the results obtained by different filter feature selection methods via aggregation techniques. Evaluations on four credit datasets show that the fusion model achieves good results.