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Computational Intelligence in Biomedicine and Bioinformatics pp 69–91Cite as

The Use of Rough Sets as a Data Mining Tool for Experimental Bio-data

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 151))

Summary

The Rough Sets methodology has great potential for mining experimental data. Since its introduction by Pawlak, it has received a lot of attention in the computing community. However, due to the mathematical nature of the Rough Sets methodology, many experimental scientists lacking sufficient mathematical background have been hesitant to use it. The goal of this chapter is twofold: (1) to introduce “Rough Sets” methodology (along with one of its derivatives, “Modified Rough Sets”) in a non-mathematical fashion hoping to share the potentials of this approach with a larger group of non-computationally-oriented scientists (Mining of one specific form of implicit data within a bio-dataset is also discussed), and (2) to apply this methodology to a dataset of children with and without Attention Deficit/Hyperactivity Disorder (ADHD), to demonstrate the usefulness of the approach in patient differentiation. Discriminant Analysis statistical approach as well as the ID3 approach were also applied to the same dataset for comparison purposes to find out which approach is most effective.

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References

  1. Han, J., Kamber, M.: Data mining, Concepts and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)

    Google Scholar 

  2. Hashemi, R., Bahar, M., Early, J., Tyler, A., Young, J.: A signature-based Liver Cancer Prediction System. In: Srimani, P. (ed.) Proc. 2005 Int. Conf. on Info Tech Coding and Computing (ITCC 2005), Las Vegas, Nevada, pp. 154–160 (2005)

    Google Scholar 

  3. Young, J., Tong, W., Fang, H., Xie, Q., Pearce, B., Hashemi, R., Beger, R., Cheeseman, M., Chen, J., Chang, Y., Kodell, R.: Building an Organ-Specific Carcinogenic Database for SAR Analyses. Toxicology and Environmental Health, Part A 67, 1363–1389 (2004)

    Article  Google Scholar 

  4. Hashemi, R., Young, J.: The Prediction of Methylmercury Elimination Half-Life In Humans Using Animal Data: A Neural Network/Rough Sets Analysis. Toxicology and Environmental Health, Part A 66(23), 2227–2252 (2003)

    Article  Google Scholar 

  5. Pawlak, Z.: Rough Classification. Man-Machine Studies 20, 469–483 (1984)

    Article  MATH  Google Scholar 

  6. Fausett, L.: Fundamentals of Neural Networks: Architectures, Algorithms, and Applications. Prentice-Hall, Englewood Cliffs (1994)

    MATH  Google Scholar 

  7. Kennedy, M.P., Chua, L.: Neural Networks for Nonlinear Programming. IEEE Transac. Circuits Sys. CAS-35(5), 554–562 (1988)

    Article  MathSciNet  Google Scholar 

  8. Hashemi, R., Tyler, A., Slikker, W., Paule, M.: Profiling Through Kohonen Self-Organizing Map: The Effect of Birth Weight On The Performance Measure Of An Operant Test Battery. In: Dagli, C.H., Buczak, A.L., Ghosh, J., Embrechts, M.J., Ersoy, O. (eds.) Proc. Annie 1999 Smart Engineering System Design: Neural Networks, Fuzzy Logic, Evolutionary Programming, Complex Systems and Data Mining, St. Louis, MO, pp. 941–946 (1999)

    Google Scholar 

  9. Hashemi, R., Schafer, T., Hinson, W., Lay, J.: Identifying and Testing of Signatures for Non-Volatile Biomolecules Using The Tandom Mass Spectra. In: Proc. 1996 ACM Int. Symp. on Applied Computing, Philadelphia, PA, pp. 44–49 (1996)

    Google Scholar 

  10. Holland, J.: Genetic Algorithms. Scientific American 267(1), 66–72 (1992)

    Article  Google Scholar 

  11. Smolinski, T., Boratyn, G., Milanova, M., Zurada, J., Wrobel, A.: Evolutionary Algorithms and Rough Sets-Based Hybrid Approach to Classificatory Decomposition of Cortical Evoked Potentials. In: Alpigini, J.J., Peters, J.F., Skowron, A., Zhong, N. (eds.) RSCTC 2002. LNCS (LNAI), vol. 2475, pp. 621–628. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  12. Hashemi, R., Pearce, B., Arani, R., Hinson, W., Paule, M.: A Fusion of Rough Sets, Modified Rough Sets, and Genetic Algorithms for Hybrid Diagnostic Systems. In: Lin, T.Y., Cercone, N. (eds.) Rough Sets and Data Mining: Analysis of Imprecise Data, pp. 149–176. Kluwer Academic Publishers, Dordrecht (1997)

    Google Scholar 

  13. Zadeh, L.: A Fuzzy-Algorithmic Approach to the Definition of Complex or Imprecise Concepts. Man-Machine Studies 8, 249–291 (1976)

    Article  MATH  MathSciNet  Google Scholar 

  14. Hashemi, R., Choobineh, F., Slikker, W., Paule, M.: On Integration of Modified Rough Set and Fuzzy Logic in Classification. In: Proc. 3rd Int. Joint Conf. on Info. Sciences, Research Triangle Park, NC, pp. 255–258 (1997)

    Google Scholar 

  15. Quinlan, J.R.: Learning Efficient Classification Procedures and Their Application to Chess Endgames. In: Michalski, J.S., Carbonell, J.G., Mirchell, T.M. (eds.) Machine Learning: An Artificial Intelligence Approach, vol. 1, pp. 463–482. Morgan Kaufmann, Palo Alto (1983)

    Google Scholar 

  16. Hashemi, R., Le Blanc, L., Traywick, B.: A Decision Tree-Based Prediction of Philanthropic Giving. In: The 2007 Int. Conf. on Machine Learning and Applications (ICMLA 2007), Cincinnati, Ohio (submitted, 2007)

    Google Scholar 

  17. Razzaghi, M., Kodell, R.: Risk Assessment for Quantitative Responses Using a Mixture Model. Biometrics 56(2), 519–527 (2000)

    Article  MATH  Google Scholar 

  18. Tomaszewski, P., Håkansson, J., Grahn, H., Lundberg, L.: Statistical Models vs. Expert Estimation for Fault Prediction in Modified Code - An Industrial Case Study Source. Systems and Software 8(8), 1227–1238 (2007)

    Article  Google Scholar 

  19. Hashemi, R., Epperson, C.: The Integration of Rough Sets and Neural Network Paradigm. In: Predictive Systems. Proc. Annie 1999 Smart Engineering System Design: Neural Networks, Fuzzy Logic, Evolutionary Programming, Complex Systems and Data Mining, St. Louis, MO, pp. 499–504 (1999)

    Google Scholar 

  20. Hashemi, R., Danley, J., Bolan, B., Tyler, A., Slikker, W., Paule, M.: Info Granulation and Super Rules. In: Proc. 4th Int. Joint Conf. on Info Sciences. Research Triangle Park, NC, pp. 383–386 (1998)

    Google Scholar 

  21. Smolinski, T., Boratyn, G., Milanova, M., Buchanan, R., Prinz, A.: Hybridization of Independent Component Analysis, Rough Sets, and Multi-Objective Evolutionary Algorithms for Classificatory Decomposition of Cortical Evoked Potentials. In: Rajapakse, J.C., Wong, L., Acharya, R. (eds.) PRIB 2006. LNCS (LNBI), vol. 4146, pp. 174–183. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  22. Boratyn, G., Smolinski, T., Zurada, J., Milanova, M., Bhattacharyya, S., Suva, L.: Hybridization of Blind Source Separation and Rough Sets for Proteomic Biomarker Indentification. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 486–491. Springer, Heidelberg (2004)

    Google Scholar 

  23. Hassanien, A.E., Slezak, D.: Rough Neural Intelligent Approach for Image Classification: A Case of Patients with Suspected Breast Cancer. Hybrid Intelligent Systems 3(4), 205–218 (2006)

    MATH  Google Scholar 

  24. Hassanien, A.E.: Fuzzy Rough Sets Hybrid Scheme for Breast Cancer Detection. Image Vision Computing, Electronic Edition 25(2), 172–183 (2007)

    Article  Google Scholar 

  25. Deogun, J., Raghavan, V.V., Sever, H.: Rough Set Based Classification Methods and Extended Decision. In: Proc. 3rd Int. Workshop on Rough Sets and Soft Computing, San Jose, CA, pp. 302–309 (1994)

    Google Scholar 

  26. Grzymala-Busse, J.W.: LERS - A System for Learning from Examples based on Rough Sets, Intel Decision Supt: Handbook of Applications and Advances in Rough Set Theory, pp. 3–18. Kluwer Academic Publishing, Dordrecht (1992)

    Google Scholar 

  27. Lin, T.Y.: Neighborhood Systems-Information Granulation. In: Proc. 3rd Int. Joint Conf. on Info Sciences, Research Triangle Park, NC, vol. 3, pp. 161–164 (1997)

    Google Scholar 

  28. Hashemi, R., Epperson, C., Tyler, A., Young, J.: Knowledge Discovery from Sparse Pharmacokinetic Data. In: Proc. 2000 ACM Int. symp. on Applied Computing (SAC 2000), Como, Italy, pp. 75–79 (2000)

    Google Scholar 

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

    Article  Google Scholar 

  30. Ras, Z.W., Koo, T.: Knowledge Discovery for Intelligent Query Answering. In: Proc. 3rd Int. Joint Conf. on Info Sciences, Research Triangle Park, NC, vol. 3, pp. 367–370 (1997)

    Google Scholar 

  31. Wong, S.K.M., Ziarko, W., Ye, R.L.: Comparison of Rough-set and Statistical Methods in Inductive Learning. Man-Machine Studies 24, 53–72 (1986)

    Article  MATH  Google Scholar 

  32. Yao, Y.Y., Noroozi, N.: A Unified Model for Set-Based Computations. In: Proc. 3rd Int. Workshop on Rough Sets and Soft Computing, San Jose CA, pp. 236–243 (1994)

    Google Scholar 

  33. Ziarko, W.: Variable Precision Rough Set Model. Computer and Sys. Scie. 46, 39–59 (1993)

    Article  MATH  MathSciNet  Google Scholar 

  34. Moshkov, M.J., Skowron, A., Suraj, Z.: On Covering Attribute Sets by Reducts. In: Kryszkiewicz, M., Peters, J.F., Rybinski, H., Skowron, A. (eds.) Proc. Int. Conf. on Rough Sets and Intel. Sys. Paradigms, Warsaw, Poland, pp. 175–180 (2007)

    Google Scholar 

  35. Grzymala-Busse, J.W.: Mining Numerical Data - A Rough Set Approach. In: Kryszkiewicz, M., Peters, J.F., Rybinski, H., Skowron, A. (eds.) Proc. of Int. Conf. on Rough Sets and Intel. Sys. Paradigms, Warsaw, Poland, pp. 12–21 (2007)

    Google Scholar 

  36. Suraj, Z., Pancerz, K.: Flow Graphs as a Tool for Mining Prediction Rules of Changes of Components in Temporal Information Systems. In: Yao, J., Lingras, W.W., Szczuka, M.S., Cercone, N., Slezak, D. (eds.) Proc. of Int. Conf. on Rough Sets and Knowledge Tech., Toronto, Canada, pp. 468–475 (2007)

    Google Scholar 

  37. Renpu, L.R., Wang, Z.: Mining Classification Rules Using Rough Sets and Neural Networks. European J. Operat. Resch. 157(2), 439–448 (2004)

    Article  MATH  Google Scholar 

  38. Stefanowski, J.: On Combined Classifiers, Rule Induction and Rough Sets. In: Peters, J.F., Skowron, A., Duntsch, I., Grzymala-Busse, J.W., Orlowska, E., Polkowski, L. (eds.) Transactions on Rough Sets VI, Part I, pp. 329–350 (2007)

    Google Scholar 

  39. Paule, M.: Analysis of Brain Function Using a Battery of Schedule-Controlled Operant Behaviors. In: Weiss, B., O’Donoghue, J. (eds.) Neurobehavioral Toxicity: Analysis and Interpretations, pp. 331–338. Raven Press, New York (1994)

    Google Scholar 

  40. Ruckert, U., Kramer, S.: A Statistical Approach to Rule Learning. In: Proc. 23rd Int. Conf. on Machine Learning, pp. 785–792. Carnegie Mellon University, Pittsburgh (2006)

    Chapter  Google Scholar 

  41. Hassanien, A.E.: Rough Set Approach for Attribute Reduction and Rule Generation: a Case of Patients with Suspected Breast Cancer. J. of the Americ. Soc. for Info. Sci. and Tech. 55(11), 954–962 (2004)

    Article  Google Scholar 

  42. Pawlak, Z.: Combining Rough Sets and Bayes’ Rule. Comput. Intel. 17(3), 401–408 (2001)

    Article  MathSciNet  Google Scholar 

  43. Michalski, R.: A theory and Methodology of Inductive Learning. In: Michalski, J.S., Carbonell, J.G., Michell, T.M. (eds.) Machine Learning, pp. 83–134. Tioga Publication Co., Palo Alto (1983)

    Google Scholar 

  44. Riddle, D.L., Coovert, M.D., Elliott, L.R., Schiflett, S.G.: Potential Contributions of Rough Sets Data Analysis to Training Evaluations. Military Psychology 15(1), 41–58 (2003)

    Article  Google Scholar 

  45. Maciag, T.J., Slezak, D., Hepting, D.H.: Consumer Modelling in Support of Interface Design. In: Proc. 2006 Int. Conf. on Hybrid Info. Tech (ICHIT 2006), Cheju Island, Korea, vol. 2, pp. 153–160 (2006)

    Google Scholar 

  46. Skowron, A., Rauszer, C.: The Discernibility Matrices and Functions in Decision systems. In: Slowinski, R. (ed.) Intelligent decision support. Handbook of applications and advances of the rough sets theory, pp. 311–362. Kluwer, Dordrecht (1992)

    Google Scholar 

  47. Sloinski, R., Zopounidis, C.: Application of Rough Set Approach to Evaluation of Bankrupcy Risk. Intel. Sys. in Account, Finance & Mngmt. 4, 27–41 (1995)

    Google Scholar 

  48. Kohavi, R.: A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection. In: Proc. 14th Int. Joint Con. on Arti. Intel., vol. 2(12), pp. 1137–1143 (1995)

    Google Scholar 

  49. Efron, B., Tibshirani, R.: Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy. Statistic Sci. (1), 54–77 (1985)

    Article  MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science, Armstrong Atlantic State University, Savannah, GA 31419, USA

    Ray R. Hashemi

  2. Area Health Education Center-Pine Bluff, University of Arkansas for Medical Sciences, 4010 Mulberry Street, Pine Bluff, AR 71603, USA

    Alexander A. Tyler

  3. Department of Information Technology, Armstrong Atlantic State University, Savannah, GA 31419, USA

    Azita A. Bahrami

Authors
  1. Ray R. Hashemi
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  2. Alexander A. Tyler
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  3. Azita A. Bahrami
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Editor information

Editors and Affiliations

  1. Department of Biology, Emory University, 1510 Clifton Rd.NE, 30322, Atlanta, Georgia, USA

    Tomasz G. Smolinski

  2. Department of Computer Science, University of Arkansas at Little Rock, 2801 S.University Ave., 72204, Little Rock, Arkansas, USA

    Mariofanna G. Milanova

  3. Department of Quantitative Methods and Information Systems College of Business and Administration, Kuwait University, P.O. Box 5486, 13055, Safat, Kuwait

    Aboul-Ella Hassanien

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© 2008 Springer-Verlag Berlin Heidelberg

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Hashemi, R.R., Tyler, A.A., Bahrami, A.A. (2008). The Use of Rough Sets as a Data Mining Tool for Experimental Bio-data. In: Smolinski, T.G., Milanova, M.G., Hassanien, AE. (eds) Computational Intelligence in Biomedicine and Bioinformatics. Studies in Computational Intelligence, vol 151. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70778-3_3

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  • DOI: https://doi.org/10.1007/978-3-540-70778-3_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-70776-9

  • Online ISBN: 978-3-540-70778-3

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