Abstract
This paper presents a fraud detection model using data mining techniques such as neural networks and symbolic extraction of classification rules from trained neural network. The neural network is first trained to achieve an accuracy rate, the activation of the values in the hidden layers of the neural network is analyzed and from this analysis are generated classification rules. The proposed approach was tested on a set of data from a Colombian organization for the sending and payment of remittances, in order to identify patterns associated with fraud detection. Similarly the results of the techniques used in the model were compared with other mining techniques such as Decision Trees and Naive Bayes. A prototype software was developed to test the model, which was integrated into RapidMiner tool, which can be used as a tool for academic software.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Kou, Y., Lu, C.T., Sirwongwattana, S., Huang, Y.P.: Survey of fraud detection techniques. In: Proceedings of IEEE Intl Conference on Networking, Sensing and Control (2004)
Shen, A., Tong, R., Deng, Y.: Application of classification models on credit card fraud detection. In: International Conference on Service Systems and Service Management, pp. 1–4 (2007)
Shao, H., Zhao, H., Chang, G.: Applying data mining to detect fraud behavior in customs declaration, vol. 3 (2002)
Lu, H., Setiono, R., Liu, H.: Effective data mining using neural networks. IEEE Transactions on Knowledge and Data Engineering 8(6), 957–961 (1996)
Setiono, R., Leow, W.K.: Fernn: An algorithm for fast extraction of rules from neural networks. Applied Intelligence 12(1), 15–25 (2000)
Fu, L.: Rule generation from neural networks. IEEE transactions on systems, man and cybernetics 24(8), 1114–1124 (1994)
Craven, M.W.: Extracting comprehensible models from trained neural networks. PhD thesis, University of Wisconsin -Madison (1996)
Andrews, R., Diederich, J., Tickle, A.B.: Survey and critique of techniques for extracting rules from trained artificial neural networks. Knowledge-Based Systems 8(6), 373–389 (1995)
Mayer, H., Huber, Rohde, Tamme: Rule extraction from artificial neural networks. University Salzburg (October 12, 2006)
Towell, G., Shavlik, J., Noordewier, M.: Refinement of approximate domain theories by knowledge-based neural networks, pp. 861–866 (1990)
Thrun, S.B.: Extracting symbolic knowledge from artificial neural networks. Revised Version of Technical Research Report I-University Bonn (1994)
Mcmillan, C., Mozer, M.C., Smolensky, P.: Rule induction through integrated symbolic and subsymbolic processing. In: Advances in Neural Information Processing Systems, pp. 969–976. Morgan Kaufmann, San Francisco (1992)
Andrés Ramírez, J.G.E.L., Santamaría, W.: Análisis del proceso de giros internacionales y control de lavado de activos mediante minería de datos. Revista Tendencias en Ingeniería de Software e Inteligencia Artificial 2 (2008)
Oracle, Database sql reference-ora hash (2003), Online http://download.oracle.com/docs
Rapidminer.: Rapidminer- free library to work data mining (2002)
Quinlan, J.R.: Induction of decision trees. Machine learning 1(1), 81–106 (1986)
Alpaydin, E.: Introduction to Machine Learning. The MIT press, Cambridge (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Santamaría Ruiz, W., Guzman, E.L. (2010). Fraud Detection Model Based on the Discovery Symbolic Classification Rules Extracted from a Neural Network. In: Sidorov, G., Hernández Aguirre, A., Reyes García, C.A. (eds) Advances in Soft Computing. MICAI 2010. Lecture Notes in Computer Science(), vol 6438. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16773-7_25
Download citation
DOI: https://doi.org/10.1007/978-3-642-16773-7_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16772-0
Online ISBN: 978-3-642-16773-7
eBook Packages: Computer ScienceComputer Science (R0)