The Community for Technology Leaders
Computer Science and Information Engineering, World Congress on (2009)
Los Angeles, California USA
Mar. 31, 2009 to Apr. 2, 2009
ISBN: 978-0-7695-3507-4
pp: 237-240
Fraud detection in tax declaration plays an important role in tax assessment. Using ensemble ISGNN (Iteration learning Self-Generating Neural Network) to solve the problem of fraud detection in tax declaration is presented in this paper. An ensemble ISGNN is trained using financial data of sampled enterprises, and the trained ensemble ISGNN is then employed to detect whether tax declared by an enterprise is legitimate or not. Experimental results show that proposed approach is effective: classification precision of proposed method is 96.7742% in 31 sample data, and it is 3.22 points higher than that of SGNN. The number of samples to train ISGNN of ensemble ISGNN is one third that of SGNN.
ISGNN; Ensemble ISGNN; Fraud Detection

K. Zhang, B. Song and A. Li, "Fraud Detection in Tax Declaration Using Ensemble ISGNN," 2009 WRI World Congress on Computer Science and Information Engineering, CSIE(CSIE), Los Angeles, CA, 2009, pp. 237-240.
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