Abstract
The task of identifying the most relevant features for a credit-scoring application is a challenging task. Reducing the number of redundant and unwanted features is an inevitable task for improving the performance of a credit-scoring model. The wrapper approach is usually used in credit-scoring applications to identify the most relevant features. However, this approach suffers from the issue of subset generation and the use of a single classifier as an evaluation function. The problem here is that each classifier may give different results that can be interpreted differently. Hence, we propose an ensemble wrapper feature-selection model in this study that is based on a multi-classifier combination. In the first stage, we address the problem of subset generation by minimizing the search space through a customized heuristic. Then, a multi-classifier wrapper evaluation is applied using two-classifier-arrangement approaches in order to select a set of mutually approved sets of relevant features. The proposed method was evaluated on four credit datasets and has shown good performance as compared to individual classifier results.