Evaluating industry performance using extracted RGR rules based on feature selection and rough sets classifier

Expert Systems with Applications

Volumn 36, Issue 5, 2009, Pages 9448-9456

  Chen, You Shyang ^a   Cheng, Ching Hsue ^a  

^a NATIONAL YUNLIN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Taiwan)

Author keywords

Data mining techniques; Feature selection; Fundamental analysis; Revenue growth rate (RGR); Rough sets classifier

Indexed keywords

CLASSIFIERS; DATA MINING; ELECTRIC LOAD FORECASTING; FEATURE EXTRACTION; FUZZY SETS; INVESTMENTS; KNOWLEDGE BASED SYSTEMS; KNOWLEDGE MANAGEMENT; LEARNING SYSTEMS; ROUGH SET THEORY; STRATEGIC PLANNING;

DATA MINING TECHNIQUES; FEATURE SELECTION; FUNDAMENTAL ANALYSIS; REVENUE GROWTH RATE (RGR); ROUGH SETS CLASSIFIER;

ECONOMICS;

EID: 60849135105     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2008.12.036     Document Type: Article

References (46)

1. Abarbanell J.S., and Bushee B.J. Fundamental analysis, future earnings, and stock prices. Journal of Accounting Research 35 1 (1997) 1-24
2. Bai S.M., and Chen S.M. Evaluating students' learning achievement using fuzzy membership functions and fuzzy rules. Expert Systems with Applications 34 1 (2008) 399-410
3. Bazan J., Nguyen H.S., Nguyen S.H., Synak P., and Wróblewski J. Rough set algorithms in classification problem. In: Polkowski L., Tsumoto S., and Lin T.Y. (Eds). Rough set methods and applications (2000), Physica-Verlag, Heidelberg 49-88
4. Bazan J., and Szczuka M. RSES and RSESlib - A collection of tools for rough set, lecture notes in computer science, 2005 (2001), Springer-Verlag, Berlin (pp 106-113)
5. Bedworth, M. D., Lowe, D. (1988). Fault tolerance in multi-layer perceptrons: A preliminary study. RSRE: Pattern Processing and Machine Intelligence Division.
6. Bishop C.M. A fast procedure for re-training the multi-layer perceptron. International Journal of Neural Systems 2 3 (1991)
7. Carter R.B., Dark F.H., and Singh A.K. Underwriter reputation, initial returns, and the long-run performance of IPO stocks. The Journal of Finance 53 1 (1998) 285-311
8. Christensen R. Entropy minimal sourcebook (1980), Entropy Ltd., Lincoln, MA
9. Conan-Guez, B., & Rossi, F. (2002). Multi-layer perceptrons for functional data analysis: A projection based approach. In ICANN 2002, Madrid, Spain (pp. 667-672).
10. Condorcet method (2007). Accessed 20.10.2007.
11. Covin J.G., Green K.M., and Slevin D.P. Strategic process effects on the entrepreneurial orientation - sales growth rate relationship. Entrepreneurship Theory and Practice 30 1 (2006) 57-82
12. Duda R.O., Hart P.E., and Stork D.G. Pattern classification (2001), Wiley, New York
13. Dunham M.H. Data mining: Introductory and advanced topics (2003), Prentice Hall, Upper Saddle River, NJ
14. Edelstein H.A. Introduction to data mining and knowledge discovery. 3rd ed. (1999), Two Crows Corp., Potomac, MD
15. Fayyad U.M., Piatetsky-Shapiro G., Smyth P., and Uthurusamy R. Advances in Knowledge Discovery and Data Mining (1996)
16. Graham B. Security analysis. 3rd ed. (2004), McGraw-Hill. 0071448209
17. Grzymala-Busse J.W. LERS, a system for learning from examples based on rough sets. In: Slowinski R. (Ed). Intelligent decision support: Handbook of applications and advances in rough set theory (1992), Kluwer Academic Publishing 3-18
18. Grzymala-Busse J.W. A new version of the rule induction system LERS. Fundamental Informative 31 (1997) 27-39
19. Hall, M. A. (1998). Correlation-based feature subset selection for machine learning. Thesis submitted in partial fulfilment of the requirements of the degree of Doctor of Philosophy at the University of Waikato.
20. Hall M.A., and Holmes G. Benchmarking feature selection techniques for discrete class data mining. IEEE Transactions on Data Engineering 15 3 (2003) 1-16
21. Han J., and Kamber M. Data mining: Concepts and techniques (2000), Morgan-Kaufman, New York
22. Hand D., Mannila H., and Smyth P. Principles of data mining (2001), MIT Press, Cambridge, MA
23. Hashemi R.R., LeBlanc L.A., and Rucks C.T. A hybrid intelligent system for predicting bank holding structure. European Journal of Operational Research 109 2 (1998) 390-402
24. Ibbotson R.G., Sindelar J.L., and Ritter J.R. Initial public offerings. Journal of Applied Corporate Finance 1 (1988) 37-45
25. Jensen F.V. Bayesian networks and decision graphs (2001), Springer
26. Kononenko, I. (1994). Estimating attributes: Analysis and extensions of RELIEF. In Proceedings of the European Conference on Machine Learning (pp. 171-182).
27. Leray, P., Francois, O. (2004). BNT structure learning package: Documentation and experiments, Technical report, Laboratoire PSI, Universite et INSA de Rouen.
28. Lippmann R.P. An introduction to computing with neural nets. IEEE ASSP Magazine (1987) 4-22
29. Loth R.B. Select winning stocks using financial statements (1999), Dearborn, Chicago, IL, USA
30. Min S.H., Lee J., and Han I. Hybrid genetic algorithms and support vector machines for bankruptcy prediction. Expert Systems with Applications 31 (2006) 652-660
31. Murphy K.P. The Bayes net toolbox for Matlab. Computing Science and Statistics (2001) 331-350
32. Murphy, K. P. (2002). Bayes net toolbox, Technical report, MIT Artificial Intelligence Laboratory. .
33. Nguyen, H. S., Nguyen, S. H. (2003). Analysis of Stulong data by rough set exploration system (RSES). In: P. Berka (Ed.), Proceedings of ECML/PKDD workshop (pp. 71-82).
34. Pawlak Z. Rough sets. Informational Journal of Computer and Information Sciences 11 5 (1982) 341-356
35. Pawlak Z. Rough sets, theoretical aspects of reasoning about data (1991), Kluwer, Dordrecht, The Netherlands
36. Quinlan J.R. Induction of decision trees. Machine Learning 1 1 (1986) 81-106
37. Quinlan J.R. C4.5: Programs for machine learning (1993), Morgan Kaufmann Publishers, San Mateo, CA
38. Rossi, F., Conan-Guez, B. Fleuret, F. (2002). Functional data analysis with multi layer perceptrons. In IJCNN 2002 proceeding, Honolulu, Hawaii, 2002 (pp. 2843-2848).
39. Shiwakoti R.K., Hudson R.S., and Short H. A study of the initial returns and the aftermarket performance of initial public offerings of demutualized building societies in the UK. Applied Economics Letters 12 7 (2005) 403-409
40. Sikonja, M. R. & Kononenko, I. (1997). An adaptation of Relief for attribute estimation on regression. Machine learning. In D. Fished (Ed.), Proceedings of 14th international conference on machine learning, ICML'97, Nashville, TN, 1997.
41. Sneed J. Estimating earnings forecasting model using fundamental analysis: Controlling for differences across industries. American Business Review (1999) 17-24
42. Taiwan Stock Exchange Corporation (2007). The Taiwan Stock Exchange Corporation web site in Taiwan. Accessed 10.10.2007.
43. Vanstone, B., Finnie, G., & Tan, C., Applying fundamental analysis and neural networks in the Australian stock market. In Proceedings of the International conference on Artificial Intelligence in Science and Technology, Hobart, Tasmania, 2004 (pp. 21-25).
44. Verzilli C.J., Whittaker J.C., Stallard N., and Chasman D. A hierarchical Bayesian model for predicting the functional consequences of amino-acid polymorphisms. Applied Statistics 54 (2005) 191-206
45. Witten I.H., and Frank E. Data mining: Practical machine learning tools and techniques. 2nd ed. (2005), Morgan Kaufman Publishers, USA
46. Yu T.H.K., and Huarng K.H. A bivariate fuzzy time series model to forecast the TAIEX. Expert Systems with Applications 34 (2008) 2945-2952