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A Generic Scheme for Generating Prediction Rules Using Rough Sets

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Rough Set Theory: A True Landmark in Data Analysis

Part of the book series: Studies in Computational Intelligence ((SCI,volume 174))

Abstract

This chapter presents a generic scheme for generating prediction rules based on rough set approach for stock market prediction. To increase the efficiency of the prediction process, rough sets with Boolean reasoning discretization algorithm is used to discretize the data. Rough set reduction technique is applied to find all the reducts of the data, which contains the minimal subset of attributes that are associated with a class label for prediction. Finally, rough sets dependency rules are generated directly from all generated reducts. Rough confusion matrix is used to evaluate the performance of the predicted reducts and classes. For comparison, the results obtained using rough set approach were compared to that of artificial neural networks and decision trees. Empirical results illustrate that rough set approach achieves a higher overall prediction accuracy reaching over 97% and generates more compact and fewer rules than neural networks and decision tree algorithm.

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References

  1. Abraham, A., Nath, B., Mahanti, P.K.: Hybrid intelligent systems for stock market analysis. In: Alexandrov, V.N., Dongarra, J., Juliano, B.A., Renner, R.S., Tan, C.J.K. (eds.) ICCS-ComputSci 2001. LNCS, vol. 2074, pp. 337–345. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  2. Abraham, A., Philip, N.S., Nath, B., Saratchandran, P.: Performance analysis of connectionist paradigms for modeling chaotic behavior of stock indices. In: Second International Workshop on Intelligent Systems Design and Applications, Computational Intelligence and Applications, pp. 181–186. Dynamic Publishers Inc., USA (2002)

    Google Scholar 

  3. Abraham, A., Philip, N.S., Saratchandran, P.: Modeling chaotic behavior of stock indices using intelligent paradigms. Int. J. Neural Parallel Sci. Comput. 11(12), 143–160 (2003)

    MATH  Google Scholar 

  4. Al-Qaheri, H., Hassanien, A.E., Abraham, A.: Discovering Stock Price Prediction Rules Using Rough Sets. Neural Network World Journal (in press, 2008)

    Google Scholar 

  5. Bauer, R.J.: Genetic Algorithms and Investment Strategies. Wiley, New York (1994)

    Google Scholar 

  6. Bazan, J., Nguyen, H.S., Nguyen, S.H., Synak, P., Wróblewski, J.: Rough Set Algorithms in Classification Problem. In: Polkowski, L., Tsumoto, S., Lin, T.Y. (eds.) ICCV-WS 1999, pp. 49–88. Physica Verlag (2000)

    Google Scholar 

  7. Indranil, B.: Deciding the financial health of dot-coms using rough sets. Information and Management 43(7), 835–846 (2006)

    Article  Google Scholar 

  8. Chan, W.S., Liu, W.N.: Diagnosing shocks in stock markets of southeast Asia, Australia, and New Zealand. Math. Comput. Simulation 59(13), 223–232 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  9. Cheng, C., Chen, Y.: Classifying the segmentation of customer value via RFM model and RS theory. Expert Systems with Applications (article in progress) (2008)

    Google Scholar 

  10. Cios, K., Pedrycz, W., Swiniarski, R.: Data Mining Methods for Knowledge Discovery. Kluwer Academic Publishers, Dordrecht (1998)

    MATH  Google Scholar 

  11. Dimitras, A.I., Slowinski, R., Susmaga, R., Zopounidis, C.: Business failure prediction using rough sets. European Journal of Operational Research 114(2), 263–281 (1999)

    Article  MATH  Google Scholar 

  12. Dimitras, A.I., Slowinski, R., Susmagab, R., Zopounidisa, C.: Business failure prediction using rough sets. European Journal of Operational Research 114(2), 263–280 (1999)

    Article  MATH  Google Scholar 

  13. Dougherty, J., Kohavi, R., Sahami, M.: Supervised and Unsupervised Discritization of Continuous Features. In: Proc. of the XII International conference on Machine Learning, San Francisco, pp. 294–301 (1995)

    Google Scholar 

  14. Dougherty, J., Kohavi, R., Sahami, M.: Supervised and Unsupervised Discritization of Continuous Features. In: Proc. of the XII International conference on Machine Learning, pp. 294–301 (1995)

    Google Scholar 

  15. Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P.: From data mining to knowledge discovery: An overview. In: Fayyad, U.M., et al. (eds.) Advances in Knowledge Discovery and Data Mining, p. 129. AAAI, New York (1996)

    Google Scholar 

  16. Francis, E.S., Cao, L.J.: Modified support vector machines in financial time series forecasting. Neurocomputing 48(14), 847–861 (2002)

    MATH  Google Scholar 

  17. Francis, E.H., Lixiang, S.: Economic and financial prediction using rough set model. European Journal of Operational Research 141(3), 641–659 (2002)

    Article  MATH  Google Scholar 

  18. Gediga, G., Duentsch, I.: Statistical Techniques for Rough Set Data Analysis. In: Rough Sets: New Developments, pp. 545–565. Physica Verlag (2000)

    Google Scholar 

  19. Golan, R.: Stock market analysis utilizing rough set theory. Ph.D. Thesis, Department of Computer Science, University of Regina, Canada (1995)

    Google Scholar 

  20. Golan, R., Ziarko, W.: A methodology for stock market analysis utilizing rough set theory. In: Proceedings of the IEEE/IAFE Computational Intelligence for Financial Engineering, pp. 32–40 (1995)

    Google Scholar 

  21. Graflund, A.: Bayesian Inference Approach to Testing Mean Reversion in the Swedish Stock Market (2002) SSRN: http://ssrn.com/abstract=250089

  22. Grosan, C., Abraham, A.: Stock Market Modeling Using Genetic Programming Ensembles, Genetic Systems Programming. In: Nedjah, N., et al. (eds.) Studies in Computational Intelligence, pp. 131–146. Springer, Germany (2006)

    Google Scholar 

  23. Guoyin, W.: Rough reduction in algebra view and information view. International Journal of Intelligent Systems 18(6), 679–688 (2003)

    Article  MATH  Google Scholar 

  24. Hassanien, A.E., Abdelhafez, M.E., Own, H.S.: Rough Set Data Analysis in Knowledge Discovery: A Case of Kuwaiti Diabetic Children Patients, Advances in Fuzzy System (in press, 2008)

    Google Scholar 

  25. Jiajin, H., Chunnian, L., Chuangxin, O., Yao, Y.Y., Zhong, N.: Attribute reduction of rough sets in mining market value functions. In: IEEE/WIC International Conference Proceedings on Web Intelligence, WI 2003, pp. 470–473 (2003)

    Google Scholar 

  26. Jovina, R., Akhtar, J.: Backpropagation and Recurrent Neural Networks in Financial Analysis of Multiple Stock Market Returns. In: Proceedings of the 29th Hawaii International Conference on System Sciences (HICSS), Decision Support and Knowledge-Based Systems, vol. 2, p. 454 (1996)

    Google Scholar 

  27. Kim, K.J., Han, I.: Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index. Expert Systems Applications 19(2), 125–132 (2000)

    Article  MathSciNet  Google Scholar 

  28. Leigh, W., Modani, N., Purvis, S., Roberts, T.: Stock market trading rule discovery using technical charting heuristics. Expert Systems and Applications 23(2), 155–159 (2002)

    Article  Google Scholar 

  29. Leigh, W., Purvis, R., Ragusa, J.M.: Forecasting the NYSE composite index with technical analysis, pattern recognizer, neural network and genetic algorithm: a case study in romantic decision support. Decision Support Syst. 32(4), 361–377 (2002)

    Article  Google Scholar 

  30. Li, J., Cercone, N.: A rough set based model to rank the importance of association rules. In: Ślęzak, D., Yao, J., Peters, J.F., Ziarko, W.P., Hu, X. (eds.) RSFDGrC 2005. LNCS, vol. 3642, pp. 109–118. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  31. Lixiang, S., Loh, H.T.: Applying rough sets to market timing decisions. Decision support Systems journal 37, 583–597 (2004)

    Article  Google Scholar 

  32. Lin, C., Wang, Y., Wu, C., Chuang, C.: Developing a business failure prediction model via RST, GRA and CBR. Expert Systems with Applications (2008) (article in press)

    Google Scholar 

  33. Mani, G., Quah, K.K., Mahfoud, S., Barr, D.: An analysis of neural-network forecasts from a large-scale, real-world stock selection system. In: Proceedings of the IEEE/IAFE 1995 Conference on Computational Intelligence for Financial Engineering (CIFER 1995), pp. 72–78. IEEE, Los Alamitos (1995)

    Chapter  Google Scholar 

  34. Maqsood, M.R., Abraham, A.: Neural network ensemble method for weather forecasting. Neural Computing and Applications 13(2), 112–122 (2004)

    Article  Google Scholar 

  35. McKee, T.E.: Rough Set Bankruptcy Prediction Models versus Auditor Signalling Rates. Journal of Forecasting 22(8), 569–586 (2003)

    Article  Google Scholar 

  36. Ning, S., Ziarko, W., Hamilton, J., Cercone, N.: Using Rough Sets as Tools for Knowledge Discovery. In: Fayyad, U.M., Uthurusamy, R. (eds.) First International Conference on Knowledge Discovery and Data Mining KDD 1995, pp. 263–268. AAAI, Montreal (1995)

    Google Scholar 

  37. Pawlak, Z.: Rough Sets. Int. J. Computer and Information SCI. 11, 341–356 (1982)

    Article  MATH  MathSciNet  Google Scholar 

  38. Pawlak, Z.: Rough Sets- Theoretical aspects of Reasoning about Data. Kluwer Academic Publishers, Dordrecht (1991)

    MATH  Google Scholar 

  39. Pawlak, Z., Grzymala-Busse, J., Slowinski, R., Ziarko, W.: Rough sets. Communications of the ACM 38(11), 89–95 (1995)

    Article  Google Scholar 

  40. Pawlak, Z., Skowron, A.: Rudiments of rough sets. Information Sciences 177, 3–27 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  41. Poel, D.: Rough sets for database marketing. In: Polkowski, L., Skowron, A. (eds.) Rough Sets in Knowledge Discovery 2, pp. 324–335. Physica-Verlag (1998)

    Google Scholar 

  42. Polkowski, L.: Rough Sets: Mathematical Foundations. Physica-Verlag (2003)

    Google Scholar 

  43. Qizhong, Z.: An Approach to Rough Set Decomposition of Incomplete Information Systems. In: 2nd IEEE Conference on Industrial Electronics and Applications, ICIEA 2007, pp. 2455–2460 (2007)

    Google Scholar 

  44. Salcedo-Sanza, S., Fernandez-Villacanasa, J.L., Segovia-Vargasb, J., Bousono-Calzona, C.: Genetic programming for the prediction of insolvency in non-life insurance companies. Computers & Operations Research 32, 749–765 (2005)

    Article  Google Scholar 

  45. Shuxiang, X., Ming, Z.: Data mining - an adaptive neural network model for financial analysis. In: Third International Conference on Information Technology and Applications, ICITA 2005, vol. 1(4-7), pp. 336–340 (2005)

    Google Scholar 

  46. Skalko, C.: Rough sets help time the OEX. Journal of Computational Intelligence in Finance 4(6), 20–27 (1996)

    Google Scholar 

  47. Ślȩzak, D.: Various approaches to reasoning with frequency-based decision reducts: a survey. In: Polkowski, L., Tsumoto, S., Lin, T.Y. (eds.) Rough Sets in Soft Computing and Knowledge Discovery: New Developments, Physica Verlag (2000)

    Google Scholar 

  48. Starzyk, J.A., Dale, N., Sturtz, K.: A mathematical foundation for improved reduct generation in information systems. In: Knowledge and Information Systems Journal, pp. 131–147. Springer, Heidelberg (2000)

    Google Scholar 

  49. Swiniarski, R., Skowron, A.: Rough set methods in feature selection and recognition. Pattern Recognition Letters 24(6), 833–849 (2003)

    Article  MATH  Google Scholar 

  50. Tsaih, R., Hsu, Y., Lai, C.C.: Forecasting S&P 500 stock index futures with a hybrid AI system. Decision Support Syst. 23(2), 161–174 (1998)

    Article  Google Scholar 

  51. Van den Berg, J., Kaymak, U., Van den Bergh, W.M.: Financial markets analysis by using a probabilistic fuzzy modelling approach. Int. J. Approximate Reasoning 35(3), 291–305 (2004)

    Article  MATH  Google Scholar 

  52. Wang, Z., Li, H., Deng, X.: Empirical Study of Business Failure Prediction Based on Rough Set. In: International Conference on Management Science and Engineering, ICMSE 2006, pp. 848–852 (2006)

    Google Scholar 

  53. Ziarko, W.: Variable Precision Rough Set Model. Journal of Computer and Systems. Sciences 46(1), 39–59 (1993)

    Article  MATH  MathSciNet  Google Scholar 

  54. Ziarko, W., Golan, R., Edwards, D.: An application of datalogic/R knowledge discovery tool to identify strong predictive rules in stock market data. In: Proceedings of AAAI Workshop on Knowledge Discovery in Databases, Washington, DC, pp. 89–101 (1993)

    Google Scholar 

  55. Zhong, N., Skowron, A.: Rough set based Knowledge Discovery Process. International Journal of Applied Maths and Computer Science 11(3), 603–619 (2001)

    MATH  MathSciNet  Google Scholar 

  56. Zopounidis, C., Doumpos, M.: Multi-group discrimination using multi-criteria analysis: Illustrations from the field of finance. European Journal of Operational Research 139(2), 371–389 (2002)

    Article  MATH  MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Quantitative Methods and Information Systems College of Business Administration, Kuwait University, Kuwait

    Hameed Al-Qaheri & Aboul Ella Hassanien

  2. Information Technology Department, FCI, Cairo University, 5 Ahamed Zewal Street, Orman, Giza, Egypt

    Aboul Ella Hassanien

  3. Center for Quantifiable Quality of Service, Norwegian University of Science and Technology, O.S. Bragstads plass 2E, NO-7491, Trondheim, Norway

    Ajith Abraham

Authors
  1. Hameed Al-Qaheri
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  2. Aboul Ella Hassanien
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  3. Ajith Abraham
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Editors and Affiliations

  1. Norwegian University of Science and Technology, Trondheim, Norway

    Ajith Abraham

  2. School of Information Technology and Engineering (SITE), Central University of Las Villas, Villa Clara, Cuba

    Rafael Falcón

  3. Department of Computer Science, Central University of LasVillas, 54830, Villa Clara, Cuba

    Rafael Bello

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Al-Qaheri, H., Hassanien, A.E., Abraham, A. (2009). A Generic Scheme for Generating Prediction Rules Using Rough Sets. In: Abraham, A., Falcón, R., Bello, R. (eds) Rough Set Theory: A True Landmark in Data Analysis. Studies in Computational Intelligence, vol 174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89921-1_6

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  • DOI: https://doi.org/10.1007/978-3-540-89921-1_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-89920-4

  • Online ISBN: 978-3-540-89921-1

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