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.

MultiWindow Based Ensemble Learning for Classification of Imbalanced Streaming Data Imbalanced streaming data is widely existed in real world and has attracted much attention in recent years. Most studies focus on either imbalance data or streaming data; however, both imbalance data and streaming data are always accompanied in practice. In this paper, we propose a multiwindow based ensemble learning (MWEL as short) method for the classification of imbalanced streaming data. Three types of windows are defined to store the current batch of instances, the latest minority instances and the ensemble classifier. The ensemble classifier consists of a set of latest subclassifiers, and instances each subclassifier trained on respectively. All subclassifiers are weighted before predicting new arriving instancexe2x80x99s class labels and new subclassifiers are trained if a precision is below a threshold. Extensive experiments on synthetic datasets and real world datasets demonstrate that the new approach can efficiently and efficiently classify imbalanced streaming data and outperform existing approaches.