A novel purity-based k nearest neighbors imputation method and its application in financial distress prediction. (May 2019) - Cheng, Ching-Hsue; Chan, Chia-Pang; Sheu, Yu-Jheng
Abstract: Financial distress research often has missing values problems, and the different missing values handling techniques have an impact on the classification results. Furthermore, missing values handling in the data sciences is an important issue, and the different missing values handling approaches restrict on the application and performance of the classification. In missing values research, previous studies usually focused on the accuracy of classification, however, they address less the overall performance of the different missing degrees. To obtain better accuracy and maintain the integrity of data on the classification, this study proposes a purity-based k nearest neighbor algorithm to improve the performance of the missing value imputation. To verify, this study implemented different missing degree and different noise rate experiments for demonstrating the better performance because the proposed method is less affected by the noise. Furthermore, this paper also implemented MAR, MCAR, and MNAR type experiments, and compared the proposed method with the listed imputation techniques. Furthermore, this study practically collected Taiwan Economic Journal (TEJ) datasets as MNAR type missing values, and then employed the proposed purity-based k nearest neighbor algorithm to build a financial distress prediction model. Finally, this study compared the proposed imputation algorithm with common imputation methods and different classifiers, the results show that the proposed imputation algorithm obtains better accuracy and more stable in different missing degrees and noise.