Lin Xiang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Lin Xiang has a total of 2 co-authors. Here are the top co-authors by number of shared publications:
- Huanzhuo Ye
Department of Information, Zhongnan University of Economics and Law, Wuhan, Hubei, China
1 shared publication - Yanping Gan
Wuhan University, Wuhan, Hubei, China
1 shared publication
Latest Publications
- Open Access
- 1 October 2019
- Published by IOP Publishing in IOP Conference Series: Materials Science and Engineering
- Vol. 612 (5) , 052051
Detecting Financial Statement Fraud Using Random Forest with SMOTE
The study explores the comparison of various classification models in detecting fraudulent financial statements (FFS). Due to the high-class imbalance in this unique domain, the samples chosen in existing researches tend to be processed not so realistically. Therefore Random Forest is adopted to learn imbalanced data, in addition, sampling with SMOTE. Some more effective measure metrics of performance are also added. The experimental dataset includes 11726 publicly available Chinese financial disclosures from 2007 to 2017, of which 1314 financial statements were accused of fraud by CSRC. The result shows that the Random Forest outperforms other algorithms: Artificial Neural Network (ANN), Logistics Regression (LR), Support Vector Machines (SVM), CART, Decision Trees, Bayesian Networks, Bagging, Stacking and Adaboost.
- 1 October 2019
- Published by IOP Publishing in IOP Conference Series: Materials Science and Engineering
- Vol. 612 (5) , 052051
Open AccessDetecting Financial Statement Fraud Using Random Forest with SMOTE
The study explores the comparison of various classification models in detecting fraudulent financial statements (FFS). Due to the high-class imbalance in this unique domain, the samples chosen in existing researches tend to be processed not so realistically. Therefore Random Forest is adopted to learn imbalanced data, in addition, sampling with SMOTE. Some more effective measure metrics of performance are also added. The experimental dataset includes 11726 publicly available Chinese financial disclosures from 2007 to 2017, of which 1314 financial statements were accused of fraud by CSRC. The result shows that the Random Forest outperforms other algorithms: Artificial Neural Network (ANN), Logistics Regression (LR), Support Vector Machines (SVM), CART, Decision Trees, Bayesian Networks, Bagging, Stacking and Adaboost.