Using novel noise filtering techniques to ensemble classifiers for credit scoring * Using noise filtering techniques to ensemble classifiers for credit scoring Credit scoring using ensemble classifications has been applied widely. Ensemble classifications benefit from each single models, however biased models and missclassification happens because of noisy data. To improve the performance of ensemble classifications, the proposed model extends the noise filtering technique that increases the accuracy of the deployed classifier. In addition, a new hybrid model combines novel noise filtering techniques and ensemble classification methods for scoring based on voting. In the hybrid model, the new label noise of data is assigned by support vector machine, then ensemble classifications to score noise. Using the novel noise filtering techniques and ensemble classifications for credit scoring obtains the high robustness and the superior performance. To be fair, some data sets for credit scoring problem are executed to compare results. The performance of the proposed model implemented by the experiment is better than the other models from 1% to 2%.

Dynamic imbalanced business credit evaluation based on Learn with sliding time window and weight sampling and FCM with multiple kernels Abstract   A good model of business credit evaluation is an important tool for risk management. Although the dynamic imbalanced data flow is more consistent with the form of collected financial data in the actual situation, existing studies seldom research financial data as this form. This paper proposes a new ensemble model for dynamic imbalanced business credit evaluation based on the improved Learn and fuzzy cmeans (FCM). To handle dynamic imbalanced financial data, Learn is improved by using a sliding time window (STW) and weight sampling (WS). This method is termed Learn.STWWS. STW can divide data with the same concept into the same dataset to solve the problem of concept drift which characteristic in dynamic data. Additionally, WS can redistribute the weights for samples of different classes to resolve the issue of imbalance. To satisfy the demand of Learn.STWWS on the prediction accuracy of a base classifier, FCM is improved by multiple kernels (MK), and is designated as MKFCM. Several kernel functions are integrated to construct MK by the mean method, and MK is adopted to improve the calculation method of distances among points for FCM. Therefore, this new ensemble model can solve the problems of dynamic data and imbalanced classes at the same time. In the empirical research, financial data from Chinese listed companies are selected to evaluate business credit risk, and the associated models are adopted to make comparative analysis. The experiment results can fully demonstrate the good performance of the new ensemble model in terms of handling dynamic imbalanced financial data.