VQSVM: A case study for incorporating prior domain knowledge into inductive machine learning

Neurocomputing

Volumn 73, Issue 13-15, 2010, Pages 2614-2623

  Yu, Ting ^a   Simoff, Simeon ^a   Jan, Tony ^a  

^a UNIVERSITY OF TECHNOLOGY SYDNEY   (Australia)

Author keywords

Imbalance data; Inductive machine learning; Prior domain knowledge; Support vector machine

Indexed keywords

DATA SETS; DOMAIN KNOWLEDGE; IMBALANCED DATA SETS; INDUCTIVE MACHINE LEARNING; KEY CHARACTERISTICS; LEARNING PHASE; LEARNING PROCESS; MACHINE LEARNING METHODS; REAL-WORLD PROBLEM; SEMIPARAMETRIC;

CONVERGENCE OF NUMERICAL METHODS; MACHINE DESIGN; PROBLEM SOLVING; SUPPORT VECTOR MACHINES; VECTOR QUANTIZATION; VECTORS;

LEARNING ALGORITHMS;

ARTICLE; BINOCULAR CONVERGENCE; HYPOTHESIS; INTERNAL CONSISTENCY; INTERNET; KNOWLEDGE; LEARNING; LEARNING ALGORITHM; MACHINE LEARNING; PARAMETRIC TEST; PRIORITY JOURNAL; SUPPORT VECTOR MACHINE;

EID: 77955331482     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2010.05.007     Document Type: Article

References (37)

1. Mitchell T. Machine Learning 1997, McGraw-Hill Science/Engineering/Math, New York, NY.
2. Galindo J., Tamayo P. Credit risk assessment using statistical and machine learning: basic methodology and risk modeling applications. Computational Economics 2000, 15:107-143.
3. T. Poggio, Lecture 2, Learning Problem and Regularization, in: Statistical Learning Theory and Applications, MIT Opencourseware, 2003.
4. Abu-Mostafa Y.S. Hints. Neural Computation 1995, 7:639-671.
5. L.S. Daniel, Selective transfer of neural network task knowledge, Ph.D. Thesis, Department of Computer Science, University of Western Ontario, London, Canada, 2000.
6. Bousquet O., Boucheron S., Lugost G. Introduction to Statistical Learning Theory. Advanced Lectures on Machine Learning, Lecture Notes in Artificial Intelligence 2004, 169-207. Springer.
7. B. Scholkopf, P. Simard, A. Smola, V. Vapnik, Prior knowledge in support vector kernels, in: Proceedings of the 1997 Conference on Advances in Neural Information Processing Systems, vol. 10, Denver, CO, USA, 1998, pp. 640-646.
8. Niyogi P., Girosi F., Poggio T. Incorporating prior information in machine learning by creating virtual examples. IEEE 1998, 86.
9. Tsang E., Yung P., Li J. EDDIE-Automation, a decision support tool for financial forecasting. Decision Support Systems 2004, 37:559-565.
10. A.V. Aho, J.E. Hopcroft, J.D. Ullman, NP-Complete Problems. The Design and Analysis of Computer Algorithms, Addison-Wesley Series in Computer Science and Information Processing. Addison-Wesley, 1974 (Chapter 10).
11. Decoste D., Scholkopf B. Training invariant support vector machines machine learning. Machine Learning 2002, 46:161-190.
12. Thrun S. Explanation based Neural Network Learning: A Lifelong Learning Approach 1996, Kluwer Academic Publishers.
13. T. Jan, T. Yu, J. Debenham, S. Simoff, Financial prediction using modified probabilistic learning network with embedded local linear models, in: CIMSA2004-IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, 2004.
14. Yoon S.-C., Henschen L.J., Park E.K., Makki S. Using domain knowledge in knowledge discovery. The Eighth International Conference on Information and Knowledge Management 1999, 243-250. ACM Press, New York, NY, USA.
15. I. Davidson, S.S. Ravi, Hierarchical clustering with constraints: theory and practice, in: The 9th European Principles and Practice of KDD, PKDD, 2005.
16. P. Simard, B. Victorri, Y.L. Cun, J. Denker, in: J. Moody (Ed.), Tangent Prop-A Formalism for Specifying Selected Invariances in an Adaptive Network Advances in Neural Information Processing Systems, vol. 4, Morgan Kaufmann, 1992.
17. O.L. Mangasarian, J.W. Shavlik, E.W. Wild, Knowledge-based kernel approximation, Journal of Machine Learning Research 5, (2004) 1127-1141.
18. Scholkopf B., Smola A. Learning with Kernels, Support Vector Machines, Regularization, Optimization, and Beyond, vol. 11, Incorporating Invariances 2002, 333-348. MIT Press.
19. X. Wu, R. Srihari, Incorporating Prior Knolwedge with Weighted Margin Support Vector Machines, KDD 04, ACM, 2004.
20. Cao L.J., Tay F.E.H. Support vector machine with adaptive parameters in financial time series forecasting. IEEE Transaction on Neural Networks 2003, 14.
21. F.R. Giorgio Fumera, Cost-sensitive Learning in Support Vector Machines, 2002.
22. Pazzani M., Brunk C., Silverstein G. A knowledge-intensive approach to learning relational concepts. The Eighth International Workshop on Machine Learning 1991, 432-436. Morgan Kaufmann.
23. Mattera D., Palmieri F., Haykin S. Semiparametric support vector machines for nonlinear model. Estimation Intelligent Signal Processing 2001, 295-305. IEEE Press. S. Haykin (Ed.).
24. V. Tresp, K. Yu, An introduction to nonparametric hierarchical Bayesian modelling with a focus on multi-agent learning, in: Proceedings of the Hamilton Summer School on Switching and Learning in Feedback Systems. Lecture Notes in Computing Science, 2004.
25. Linde Y., Buzo A., Gray R.M. An algorithm for vector quantizer design. IEEE Transactions on Communications 1980, 702-710.
26. Gersho A., Gray R.M. Vector Quantization and Signal Compression 1992, Kluwer Academic Publishers.
27. N.V. Chawla, N. Japkowics, A. Kolcz, Editorial: special issue on learning from imbalanced data sets, SIGKDD Explorations 6 (2004).
28. K. Veropoulos, C. Campbell, N. Cristianini, Controlling the sensitivity of support vector machines, in: The International Joint Conference on Artificial Intelligence (IJCAI99), Workshop ML3, 1999, pp. 55-60.
29. Karakoulas G., Shawe-Taylor J. Optimizing classifiers for imbalanced training sets. Advances in Neural Information Processing Systems 1998, 253-259. MIT Press, Cambridge, MA, USA.
30. Lin Y., Lee Y., Wahba G. Support vector machines for classification in nonstandard situations. Machine Learning 2002, 46:191-202.
31. R. Akbani, S. Kwek, N. Japkowicz, Applying support vector machines to imbalanced datasets, in: Proceedings of the 15th European Conference on Machine Learning (ECML), 2004, pp. 39-50.
32. L. Tang, H. Liu, Bias analysis in text classification for highly skewed data, in: The International Conference on Data Mining, 2005.
33. N. Japkowicz, Class imbalances: Are we focusing on the right issue? in: Workshop on Learning From Imbalanced Data Sets II, 2003.
34. Y. Li, D. Stokes, J. Hamilton, Listed company auditor self-selection bias and audit fee premiums, 2005.
35. M. Kubat, S. Matwin, Addressing the curse of imbalanced data sets: one-sided sampling, in: Proceedings of the Fourteenth International Conference on Machine Learning, 1997, pp. 179-186.
36. Towell G., Shavlik J. An approach to combining explanation-based and neural learning algorithms. Connection Science 1989, 1:233-255.
37. Russell S.J. Prior knowledge and autonomous learning. Robotics and Autonomous Systems 1991, 8:145-159.