ICTACT Journal on Soft Computing

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Financial forecasting Using Decision Tree (reptree&C4.5) and Neural Networks (K*) for Handling the Missing Values


Affiliations
1 School of Computer Science and Engineering, Lovely Professional University, India
2 Department of Computer Science, Pondicherry University, India
     

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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.

Keywords

Bankruptcy Prediction, Missing Values, Decision Tree (REPTree, C4.5), ANN (K*).
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  • Financial forecasting Using Decision Tree (reptree&C4.5) and Neural Networks (K*) for Handling the Missing Values

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Authors

J. Jayanthi
School of Computer Science and Engineering, Lovely Professional University, India
Gurpreet Kaur
School of Computer Science and Engineering, Lovely Professional University, India
K. Suresh Joseph
Department of Computer Science, Pondicherry University, India

Abstract


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.

Keywords


Bankruptcy Prediction, Missing Values, Decision Tree (REPTree, C4.5), ANN (K*).

References