Dynamic weighted ensemble classification for credit scoring using Markov Chain

As the ensemble methods achieve significantly better performances than individual models do, they have been widely applied to credit scoring. However, most of them employ a static combiner to combine base classifiers, which do not consider the base classifiers’ characters and their dynamic classification ability. Though some dynamic ensemble methods are proposed, they need to produce a large number of base classifiers or employ a fixed combiner, which limit the generality of the ensemble methods. In this paper, we propose a new dynamic weighted ensemble method for credit scoring. Markov Chain is employed to model the change of each classifier’s classification ability and build a dynamic weighted trainable combiner, which dynamically assign weights to the base classifiers for each sample in the testing set. Through eight credit data sets from the real world, the experimental study demonstrates the ability and efficiency of the dynamic weighted ensemble method to improve prediction performance against the benchmark models, including some well-known individual classifiers and dynamic ensemble methods. Moreover, the proposed method can effectively decrease the misclassification cost, which can reduce risks for the financial institutions.