FINANCIAL FORECASTING USING DECISION TREE (REPTree & C4.5) AND NEURAL NETWORKS (K*) FOR HANDLING THE MISSING VALUES - J Jayanthi; Gurpreet Kaur; K Suresh Joseph (ICTACT Journal on Soft Computing, 2017)
Missing values are a widespread problem in data analysis. The purpose of this paper is to design a model to handle the missing values in predicting financial health of companies. Forecasting business failure is an important and challenge task for both academic researchers and business practitioners. In this study, we compare the classification of accuracy in decision tree methods (REP tree, C4.5) and with ANN method (K*) to handle the missing values.