Small sample-oriented case-based kernel predictive modeling and its economic forecasting applications under n-splits-k-times hold-out assessment

In most situations, we can only collect small samples for modeling specific economic forecasting problems. Thus, the availability of accurate predictive model for small samples is vital for solving these problems. This research makes an early investigation on the small sample-oriented case-based kernel predictive method (SSOCBKPM) by integrating support vector machine in case reuse of case-based reasoning, and conducts an early application of SSOCBKPM in binary economic forecasting under the n-splits-k-times hold-out method. After business cases consisting of small samples are represented, the most similar cases from small samples to the current problem are retrieved from small case base. The most similar cases retrieved from small samples are then mapped into a higher dimensional space by kernel function to be candidate support vectors, in which dimension a hyper-plane of support vector machine is constructed by reusing the most similar cases. Two datasets for firm failure prediction and one dataset for loan failure prediction were used to test performance of SSOCBKPM. 100 times' random selection of each of the 20%, 35%, 50%, 65%, and 80% of the total samples are respectively used in training to simulate the availability of samples. The results indicate that SSOCBKPM improves accuracy, stability and sensitivity of the classical CBR significantly; and improves performance of SVM significantly when the volume of the training samples becomes smaller. The SSOCBKPM is more useful in economic forecasting than case-based reasoning and support vector machine since the proportion of available samples is commonly small and less than 50% of the population.