Rough set based decision support—models easy to interpret

Advanced Information and Knowledge Processing

Volumn , Issue 9781447127598, 2012, Pages 95-112

  Widz, Sebastian ^a,b   Ślęzak, Dominik ^c,d  

^a SYSTEMS RESEARCH INSTITUTE   (Poland)

^b XPLUS SA   (Poland)

^c UNIVERSITY OF WARSAW   (Poland)

^d INFOBRIGHT INC   (Poland)

Author keywords

Decision support; Ensemble feature selection; Ensembles; Feature subset selection; Rough sets

Indexed keywords

EID: 85032194969     PISSN: 16103947     EISSN: 21978441     Source Type: Book Series    
DOI: 10.1007/978-1-4471-2760-4_6     Document Type: Chapter

References (60)

1. Kivijärvi, H., Tuominen, M.: A decision support system for evaluating intangible investments. Comput. Ind. Eng. 25(1-4), 353-356 (1993)
2. Januszewski, A.: The model and tools for creating a decision support system with the application of activity based costing (ABC-DSS). In: Proc. of the 6th Int. Conf. on Business Information Systems, Colorado Springs, USA (2003)
3. Tan, D., Sim, Y., Yeoh, W.: Applying feature selection methods to improve the predictive model of a direct marketing problem. In: Proc. of the 2nd International Conference on Software Engineering and Computer Systems (ICSECS), pp. 155-167. Springer, Berlin (2011)
4. Wauters, T., Verbeeck, K., Verstraete, P., Vanden Berghe, G., De Causmaecker, P.: Real-world production scheduling for the food industry: an integrated approach. Eng. Appl. Artif. Intell. (2011). doi:10.1016/j.engappai.2011.05.002
5. Doyle, L.: Supply chain information systems and decision support. In: Adam, F., Humphreys, P. (eds.) Encyclopedia of Decision Making and Decision Support Technologies. Information Science Reference, pp. 814-821. IGI Global, Hershey (2008)
6. Burstein, F., Holsapple, C.W.: Handbook on Decision Support Systems, Parts 1 & 2, 1st edn. Springer, Berlin (2008)
7. Klösgen, W., Żytkow, J.M. (eds.): Handbook of Data Mining and Knowledge Discovery. Oxford University Press, London (2002)
8. Kovalerchuk, B., Vityaev, E., Yupusov, H.: Symbolic methodology in numeric data mining: relational techniques for financial applications. Comput. Engin. Finance Sci. J. (2002)
9. Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proc. of the 1993 ACM SIGMOD Int. Conf. on Management of Data. ACM, New York (1993)
10. Nair, B.B., Mohandas, V.P., Sakthivel, N.R.: A decision tree-rough set hybrid system for stock market trend prediction. Int. J. Comput. Appl. 6(9), 1-6 (2010)
11. Hung, Y.H.: A neural network classifier with rough set-based feature selection to classify multiclass IC package products. Adv. Eng. Inform. 23(3), 348-357 (2009)
12. Lingras, P., Butz, C., Bhalchandra, P.: Financial series forecasting using dual rough support vector regression. In: Peters, G., Lingras, P., Ślȩzak, D., Yao, Y. (eds.) Rough Sets: Selected Methods and Applications in Management and Engineering. Springer, Berlin (2012)
13. Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning About Data. Kluwer Academic, Dordrecht (1991)
14. Pawlak, Z., Skowron, A.: Rudiments of rough sets. Inf. Sci. 177(1), 3-27 (2007)
15. Banka, H., Mitra, S.: Feature selection, classification and rule generation using rough sets. In: Peters, G., Lingras, P., Ślȩzak, D., Yao, Y. (eds.) Rough Sets: Selected Methods and Applications in Management and Engineering. Springer, Berlin (2012)
16. Świniarski, R.W., Skowron, A.: Rough set methods in feature selection and recognition. Pattern Recognit. Lett. 24(6), 833-849 (2003)
17. Moshkov, M., Piliszczuk, M., Zielosko, B.: Partial Covers, Reducts and Decision Rules in Rough Sets. Springer, Berlin (2010)
18. Ślȩzak, D.: Rough sets and functional dependencies in data: foundations of association reducts. In: Transactions on Computational Science 5. LNCS, vol. 5540, pp. 182-205. Springer, Berlin (2009)
19. Jensen, R., Shen, Q.: Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches. Wiley, Hoboken (2008)
20. Yao, Y., Zhao, Y., Wang, J.: On reduct construction algorithms. In: Transactions on Computational Science 2. LNCS, vol. 5150, pp. 100-117. Springer, Berlin (2008)
21. Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157-1182 (2003)
22. Kohavi, R., Sommerfeld, D.: Feature subset selection using the wrapper method: overfitting and dynamic search space topology. In: Proc. of KDD 1995, pp. 192-197 (1995)
23. Bazan, J.G., 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.) New Developments in Knowledge Discovery in Information Systems, pp. 49-88. Physica-Verlag, Heidelberg (2000)
24. Wróblewski, J.: Adaptive aspects of combining approximation spaces. In: Pal, S.K., Polkowski, L., Skowron, A. (eds.) Rough-Neural Computing, pp. 139-156. Springer, Berlin (2004)
25. Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123-140 (1996)
26. Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms. Wiley, New York (2004)
27. Ślȩzak, D., Widz, S.: Rough-set-inspired feature subset selection, classifier construction, and rule aggregation. In: Proc. of RSKT. LNCS, vol. 6954, pp. 81-88. Springer, Berlin (2011)
28. Lim, A.H.L., Lee, C.: Processing online analytics with classification and association rule mining. Knowl.-Based Syst. 23(3), 248-255 (2010)
29. Bao, Y., Zhang, L.: Decision support system based on data warehouse. World Acad. Sci. Eng. Technol. 71, 172-176 (2010)
30. Jukic, N., Jukic, B., Malliaris, M.: Online analytical processing (OLAP) for decision support. In: Handbook on Decision Support Systems 1. Int. Handbooks on Information Systems (2008)
31. Panchal, H.M.: Online analytical mining: architecture and challenges. IUP J. Syst. Manag. 8(2), 40-45 (2010)
32. Holsapple, C.W., Sena, M.P.: The decision-support characteristics of ERP systems. Int. J. Hum.-Comput. Interact. 16(1), 101-123 (2003)
33. Liu, B.C., Tzeng, G.H., Hsieh, C.T.: Energy planning and environmental quality management: a decision support system approach. Energy Econ. 14(4), 302-307 (1992)
34. Sprague, R.H., Watson, H.J.: Decision Support for Management. Prentice-Hall, Englewood Cliffs (1996)
35. Mora, M., Forgionne, G., Gupta, J.N.D. (eds.): Decision Making Support Systems: Achievements and Challenges for the New Decade. Idea Group Publishing, Hershey (2003)
36. Averweg, U.R.: Decision support systems and decision-making processes. In: Adam, F., Humphreys, P. (eds.) Encyclopedia of Decision Making and Decision Support Technologies. Information Science Reference, pp. 218-224. IGI Global, Hershey (2008)
37. Paprika, Z.Z.: Analysis and intuition in strategic decision making: the case of California. In: Adam, F., Humphreys, P. (eds.) Encyclopedia of Decision Making and Decision Support Technologies. Information Science Reference, pp. 20-28. IGI Global, Hershey (2008)
38. Han, J., Kamber, M.: Data Mining: Concepts and Techniques, 2nd edn. Morgan Kaufmann, San Mateo (2006)
39. Blum, A., Langley, P.: Selection of relevant features and examples in machine learning. Artif. Intell. 97, 245-271 (1997)
40. Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artif. Intell. J. 97(1-2), 273-324 (1997)
41. Lal, T., Chapelle, O., Weston, J., Elisseeff, A.: Embedded methods. In: Guyon, I., Gunn, S., Nikravesh, M., Zadeh, L. (eds.) Feature Extraction, Foundations and Applications. Springer, Berlin (2005)
42. Janusz, A., Stawicki, S.: Applications of approximate reducts to the feature selection problem. In: Proc. of RSKT. LNCS, vol. 6954, pp. 45-50. Springer, Berlin (2011)
43. Grzymała-Busse, J.W., Ziarko, W.: Rough sets and data mining. In: Wang, J. (ed.) Encyclopedia of Data Warehousing and Mining, 2nd edn., pp. 1696-1701. IGI Global, Hershey (2009)
44. Yao, Y.: Three-way decisions using rough sets. In: Peters, G., Lingras, P., Ślȩzak, D., Yao, Y. (eds.) Rough Sets: Selected Methods and Applications in Management and Engineering. Springer, Berlin (2012)
45. Dietterich, T.G.: Ensemble methods in machine learning. In: Proc. of Int. Workshop on Multiple Classifier Systems (MCS). LNCS, vol. 1857, pp. 1-15. Springer, Berlin (2000)
46. Polikar, R.: Ensemble based systems in decision making. IEEE Circuits Syst. Mag. 6, 21-45 (2006)
47. Kuncheva, L.I., Whitaker, C.: Measures of diversity in classifier ensembles. Mach. Learn. 51, 181-207 (2003)
48. Sollich, P., Krogh, A.: Learning with ensembles: how overfitting can be useful. In: Touretzky, D., Mozer, M., Hasselmo, M. (eds.) Advances in Neural Information Processing Systems, vol. 8, pp. 190-196. MIT Press, Cambridge (1996)
49. Dietterich, T.G., Bakiri, G.: Solving multiclass learning problems via error-correcting output codes. J. Artif. Intell. Res. 2, 263-286 (1995)
50. Nguyen, S.H., Bazan, J.G., Skowron, A., Nguyen, H.S.: Layered learning for concept synthesis. In: Transactions on Rough Sets I. LNCS, vol. 3100, pp. 187-208. Springer, Berlin (2004)
51. Ślȩzak, D., Janusz, A.: Ensembles of bireducts: towards robust classification and simple rep-resentation. In: Proc. of Int. Conf. on Future Generation of Information Technology (FGIT). LNCS, vol. 7105. Springer, Berlin (2011)
52. Freund, Y.: Boosting a weak learning algorithm by majority. Inf. Comput. 121(2), 256-285 (1995)
53. Kuncheva, L.I., Whitaker, C.: Feature subsets for classifier combination. An enumerative experiment. In: Multiple Classifier Systems. Proc. of the 2nd Int. Workshop MSC 2001. LNCS, vol. 2096, pp. 228-237. Springer, Berlin (2001)
54. Saeys, Y., Abeel, T., Van de Peer, Y.: Robust feature selection using ensemble feature selection techniques. In: Proc. of ECML PKDD 2008, Part II. LNAI, vol. 5212, pp. 313-325. Springer, Berlin (2008)
55. Ho, T.K.: The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 20(8), 832-844 (1998)
56. Oliveira, L.S., Morita, M., Sabourin, R.: Feature selection for ensembles using the multiobjective optimization approach. Stud. Comput. Intell. 16, 49-74 (2006)
57. Stefanowski, J.: An experimental study of methods combining multiple classifiers-diversified both by feature selection and bootstrap sampling. In: Atanassov, T.K., Kacprzyk, J., Krawczak, M., Szmidt, E. (eds.) Issues in the Representation and Processing of Uncertain and Imprecise Information, pp. 337-354. Akademicka Oficyna Wydawnicza EXIT, Warsaw (2005)
58. Tang, E.K., Suganthan, P.N., Yao, X.: An analysis of diversity measures. Mach. Learn. 65(1), 247-271 (2006)
59. Tsymbal, A., Pechenizkiy, M., Cunningham, P.: Diversity in search strategies for ensemble feature selection. Inf. Fusion 6(1), 83-98 (2005)
60. Ślȩzak, D., Widz, S.: Evolutionary inspired optimization of feature subset ensembles. In: Proc. of NaBIC, pp. 437-442 (2010)