Title :
The application of C4.5 algorithm based on SMOTE in financial distress prediction model
Author_Institution :
Dept. of Inf. Technol., Jinling Inst. of Technol., Nanjing, China
Abstract :
Datasets used in financial distress forecast are unbalanced. The traditional method gets lower predict accuracy especially in small samples of unbalanced datasets. The datasets are balanced with SMOTE method and then classified with the classical decision tree algorithm C4.5. The results show that the prediction model based on C4.5 algorithm gets the better performance.
Keywords :
data mining; decision trees; financial management; C4.5 algorithm; SMOTE method; decision tree algorithm; financial distress prediction model; Accuracy; Algorithm design and analysis; Classification algorithms; Decision trees; Machine learning; Prediction algorithms; Predictive models; Decision Tree; financial distress prediction; smote; unbalanced dataset;
Conference_Titel :
Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC), 2011 2nd International Conference on
Conference_Location :
Deng Leng
Print_ISBN :
978-1-4577-0535-9
DOI :
10.1109/AIMSEC.2011.6011460
