Self-Adaptive bagging approach to credit rating

We propose an enhanced bagging strategy based on self adaptive learning. Recent work in computational biology has seen an increasing use of ensemble learning methods due to their unique advantages in dealing with small sample size, high-dimensionality, and complex data structures. The proposed strategy has a unique advantage in dealing with classification tasks (i.e., credit rating in this study) with data on a relatively small sample size but a large number of heterogeneously distributed features. The self-organising learning mechanism makes the traditional bagging strategy more efficient in terms of model structure. Each submodel in the bagging network becomes a self-adapting base learner in credit rating. In order to prove the applicability and to measure the performance of the proposed method, we carried out a validation using three different traditional algorithms over both artificial datasets and real market data. The results of the proposed algorithm are, on average, better in most of the comparative experiments, while maintaining a model structure that is less complex.