An Intelligent System for Business Data Mining
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초록
Mining high-dimensional business data is a challenging problem. Particularly in bankruptcy predictions, we need to analyze large amounts of information from financial statements and stock markets. This paper proposes a new strategy to deal with the problem. Because of the highly correlation among financial information, this study employed a technique called generalized discriminant analysis (GDA) to identify important features and reduce the data dimension. GDA is a nonlinear discriminant analysis using kernel function operator. It’s easy to deal with a wide class of nonlinearity in financial data, and can reduce the computational loading of subsequent prediction classifier. Due to the promising success of kernel machines in many applications, this study utilized a generalized multiple kernel machine (GMKM) to serve as the predictor. Combining the strengths of GDA and GMKM, our system robustly outperforms traditional prediction systems.
Abstract
Mining high-dimensional business data is a challenging problem. Particularly in bankruptcy predictions, we need to analyze large amounts of information from financial statements and stock markets. This paper proposes a new strategy to deal with the problem. Because of the highly correlation among financial information, this study employed a technique called generalized discriminant analysis (GDA) to identify important features and reduce the data dimension. GDA is a nonlinear discriminant analysis using kernel function operator. It’s easy to deal with a wide class of nonlinearity in financial data, and can reduce the computational loading of subsequent prediction classifier. Due to the promising success of kernel machines in many applications, this study utilized a generalized multiple kernel machine (GMKM) to serve as the predictor. Combining the strengths of GDA and GMKM, our system robustly outperforms traditional prediction systems.
참고문헌 (14)
- Ahn, B. S., "The integrated methodology of rough set theory and artificial neural network for business failure prediction", Expert Systems with Applications, 18(2), pp.65~74 (2000)
- Baudat, G., "Generalized Discriminant Analysis Using a Kernel Approach", Neural Computation, 12(10), pp.2385~2404 (2000)
- Bellman, R., "Adaptive Control Processes: A Guided Tour" (1961), Princeton University Press
- Chapelle, O., "Choosing multiple parameters for Support Vector Machines", Machine Learning, 46, pp.131~159 (2002)
- Fukunaga, K., "Introduction to Statistical Pattern Recognition" (1990), Academic Press
- Hua, Z., "Predicting corporate financial distress based on integration of support vector machine and logistic regression", Expert Systems with Applications, 33(2), pp.434~440 (2007)
- Lanckriet, G. R. G., "Learning the kernel matrix with semidefinite programming", Journal of Machine Learning Research, 5, pp.27~72 (2004)
- Schoelkopf, B., "Advances in kernel methods" (1999)
- Vapnik, V. N., "The nature of statistical learning theory" (1999), Springer
- Varma, M., "More generality in efficient multiple kernel learning", Proceedings of the International Conference on Machine Learning, pp.1065~1072 (2009)
- Varma, M., "Learning the discriminative power-invariance trade-off", International Conference on Computer Vision (2007)
- Wang, W., "Data Mining and Knowledge Discovery Handbook", pp.793~799 (2005)
- Witten, I. H., "Data Mining: Practical Machine Learning Tools and Techniques" (2005)
- Wu, C. H., "Variable Selection Method Affects SVM-based Models in Bankruptcy Prediction", 9th Joint International Conference on Information Sciences (2006)