Abstract
The firms have need of a control mechanism in order to analyse whether they are achieving their goals. A tool that automates the business control process has been developed based on a case-based reasoning system. The objective of the system is to facilitate the process of internal auditing. The system analyses the data that characterises each one of the activities carried out by the firm, then determines the state of each activity and calculates the associated risk. This system uses a different problem solving method in each of the steps of the reasoning cycle. A Maximum Likelihood Hebbian Learning-based method that automates the organization of cases and the retrieval stage of case-based reasoning systems is presented in this paper. The proposed methodology has been derived as an extension of the Principal Component Analysis, and groups similar cases, identifying clusters automatically in a data set in an unsupervised mode. The system has been tested in 10 small and medium companies in the textile sector, located in the northwest of Spain and the results obtained have been very encouraging.
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References
Aamodt, A., Plaza, E.: Case-Based Reasoning: foundational Issues, Methodological Variations, and System Approaches. AICOM 7(1) (1994)
Borrajo, L.: Sistema híbrido inteligente aplicado a la auditoría de los sistemas internos. Phd Thesis. Teses de doutoramento da Universidade de Vigo. Universidade de Vigo (Spain) (December 2003) ISBN: 84-8158-274-3
Corchado, E., Fyfe, C.: Maximum and Minimum Likelihood Hebbian Rules as a Exploratory Method. In: 9th International Conference on Neural Information Processing, November 18-22, 2002, Singapore (2002)
Corchado, E., MacDonald, D., Fyfe, C.: Optimal Projections of High Dimensional Data. In: ICDM 2002. The 2002 IEEE International Conference on Data Mining, Maebashi TERRSA, Maebashi City, December 9-12, 2002, IEEE Computer Society, Los Alamitos (2002)
Corchado, J.M., Borrajo, L., Pellicer, M.A., Yáñez, J.C.: Neuro-symbolic System for Business Internal Control. In: Perner, P. (ed.) ICDM 2004. LNCS (LNAI), vol. 3275, pp. 302–9743. Springer, Heidelberg (2004)
Corchado, J.M., Díaz, F., Borrajo, L., Fdez-Riverola, F.: Redes Neuronales Artificiales: Un enfoque práctico. Departamento de publicaciones de la Universidad de Vigo (2000)
Diaconis, P., Freedman, D.: Asymptotics of Graphical Projections. The Annals of Statistics 12(3), 793–815 (1984)
Fdez-Riverola, F., Corchado, J.M.: FSfRT: Forecasting System for Red Tides. Applied Intelligence. Special Issue on Soft Computing in Case-Based Reasoning 21(3), 251–264 (2004)
Fritzke, B.: Fast Learning with Incremental RBF Networks. Neural Processing Letters 1(1), 2–5 (1994)
Fyfe, C., Baddeley, R.: Non-linear data structure extraction using simple Hebbian networks. Biological Cybernetics 72(6), 533–541 (1995)
Fyfe, C., Corchado, E.: Maximum Likelihood Hebbian Rules. 10th European Symposium on Artificial Neural Networks, ESANN 2002, Bruges, April 24-25-26 (2002a)
Fyfe, C., Corchado, E.: A New Neural Implementation of Exploratory Projection Pursuit. In: Yin, H., Allinson, N.M., Freeman, R., Keane, J.A., Hubbard, S. (eds.) IDEAL 2002. LNCS, vol. 2412, pp. 12–14. Springer, Heidelberg (2002b)
Fyfe, C., MacDonald, D.: ε-Insensitive Hebbian learning, Neuro Computing (2001)
Hunt, J., Miles, R.: Hybrid case-based reasoning. The Knowledge Engineering Review 9(4), 383–397 (1994)
Hyvärinen, A.: Complexity Pursuit: Separating interesting components from time series. Neural Computation 13, 883–898 (2001)
Hyvärinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. Wiley, Chichester (2002)
Karhunen, J., Joutsensalo, J.: Representation and Separation of Signals Using Non-linear PCA Type Learning. Neural Networks 7, 113–127 (1994)
Kashyap, R.L., Blaydon, C.C., Fu, K.S.: Stochastic Approximation. In: Mendel, J.M. (ed.) A Prelude to Neural Networks: Adaptive and Learning Systems, Prentice Hall, Englewood Cliffs (1994)
Kolodner, J.: Case-Based Reasoning. Morgan Kaufmann, San Francisco (1993)
Lai, P.L., Charles, D., Fyfe, C.: Seeking Independence using Biologically Inspired Artificial Neural Networks. In: Girolami, M.A. (ed.) Developments in Artificial Neural Network Theory: Independent Component Analysis and Blind Source Separation, Springer, Heidelberg (2000)
Lenz, M., Bartsch-Spörl, B., Burkhard, H.-D., Wess, S. (eds.): Case-Based Reasoning Technology. LNCS (LNAI), vol. 1400. Springer, Heidelberg (1998)
Mas, J., Ramió, C.: La Auditoría Operativa en la Práctica. Ed. Marcombo, Barcelona (1997)
Medsker, L.R.: Hybrid Intelligent Systems. Kluwer Academic Publishers, Dordrecht (1995)
Oja, E.: Neural Networks, Principal Components and Subspaces. International Journal of Neural Systems 1, 61–68 (1989)
Oja, E., Ogawa, H., Wangviwattana, J.: Principal Components Analysis by Homogeneous Neural Networks, part 1, The Weighted Subspace Criterion. IEICE Transaction on Information and Systems E75D, 366–375 (1992)
Smola, A.J., Scholkopf, B.: A Tutorial on Support Vector Regression. Technical Report NC2-TR-1998-030, NeuroCOLT2 Technical Report Series (1998)
Watson, I.: Applying Case-Based Reasoning: Techniques for Enterprise Systems. Morgan Kaufmann, San Francisco (1997)
Watson, I., Marir, F.: Case-Based Reasoning: A Review. The Knowledge Engineering Review 9(4), 355–381 (1994)
Xu, L.: Least Mean Square Error Reconstruction for Self-Organizing Nets. Neural Networks 6, 627–648 (1993)
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Borrajo, M.L., Corchado, J.M., Corchado, E.S., Pellicer, M.A. (2007). Neural Business Control System. In: Perner, P. (eds) Advances in Data Mining. Theoretical Aspects and Applications. ICDM 2007. Lecture Notes in Computer Science(), vol 4597. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73435-2_19
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DOI: https://doi.org/10.1007/978-3-540-73435-2_19
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