Rough sets methods in feature reduction and classification

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1. Almuallim H. and Dietterich T.G. (1991): Learning with many irrelevant features. — Proc. 9th Nat. Conf. Artificial Intelligence, Menlo Park, CA, AAAI Press, pp.574–552. Zbl0942.68657
2. Atkeson C.G. (1991): Using locally weighted regression for robot learning. — Proc. IEEE Int. Conf. Robotics and Automation, pp.958–963 
3. Bazan J., Skowron A. and Synak P. (1994a): Market data analysis: A rough set approach. — ICS Res. Rep., No.6, Warsaw University of Technology, Warsaw, Poland. 
4. Bazan J., Skowron A. and Synak P. (1994b): Dynamic reducts as a tool for extracting laws from decision tables. — Proc. Symp. Methodologies for Intelligent Systems, Charlotte, NC, pp.16–19. 
5. Bishop C.M. (1995): Neural Networks for Pattern Recognition. — Oxford: Oxford Press Zbl0868.68096
6. Blumer A., Ehrenfeucht A., Haussler D. and Warmuth M.K. (1987): Occam’s razor. — Inf. Process. Lett., Vol.24, pp.377–380. Zbl0653.68084
7. Diamentras K.I. and Kung S.Y. (1996): Principal Component Neural Networks. Theory and Applications. — New York: Wiley. 
8. Cios K., Pedrycz W. and Świniarski R.W. (1998): Data Mining Methods in Knowledge DisHUK covery. — Boston/Dordrecht/London: Kluwer Academic Publishers. Zbl0912.68199
9. Doak J. (1992): An evaluation of feature selection methods and their application to computer security. — Tech. Rep., No.CSE-92-18, University of California at Davis. 
10. Duda R.O. and Hart P.E. (1973): Pattern Recognition and Scene Analysis. — New York: Wiley. Zbl0277.68056
11. Fisher R.A. (1936): The use of multiple measurements in taxonomy problems. — Annals of Eugenics, Vol.7, pp.179–188. 
12. Fukunaga K. (1990): Introduction to Statistical Pattern Recognition. — New York: Academic Press. Zbl0711.62052
13. Geman S., Bienenstock E. and Doursat R. (1992): Neural networks and the bias/variance dilemma. — Neural Comput., Vol.4, No.1, pp.1–58. 
14. Holland J.H. (1992): Adaptation of Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence. — MIT Press. 
15. Hong Z.Q. (1991): Algebraic Feature Extraction of Image for Recognition. — Pattern RecogHUK nition, Vol.24, No.3, pp.211–219. 
16. Jain A.K. (1989): Fundamentals of Digital Image Processing. — New York: Prentice Hall. Zbl0744.68134
17. John G., Kohavi R. and Pfleger K. (1994): Irrelevant features and the subset selection probHUK lem. — Proc. 11th Int. Conf. Machine Learning (ICML-94), pp.121–129. 
18. Karhunen K. (1947): Uber lineare methoden in der Wahrscheinlichkeitsrechnung. — Annales Acedemiae Scientiarum Fennicae, Series AI: Mathematica-Physica, 3rd Ed.: Van NosHUK trand, pp.373–379. 
19. Kira K. and Rendell L.A. (1992): A practical approach to feature selection. — Proc. 9th Int. Workshop Machine Learning, Aberdeen, Scotland, pp.259–256. 
20. Kittler J. (1986): Feature selection and extraction, In: Handbook of Pattern Recognition and Image Processing (T.Y. Young and K.S. Fu, Eds.), San Diego: Academic Press, pp.59–83. 
21. Kohonen T. (1990): The Self-Organizing Map. — Proc. IEEE, Vol.78, pp.1464–1480. 
22. Kononenko I. (1994): Estimating attributes: Analysis and extension of Relief. — Proc. Europ. Conf. Machine Learning. 
23. Langley P. and Sage S. (1994): Selection of relevant features in machine learning. — Proc. AAAI Fall Symp. Relevance, pp.140–144. 
24. Lewler E.L. and Wood D.E. (1966): Branch and bound methods: A survey. — Oper. Res., Vol.149, pp.4. 
25. Liu H. and Setiono R. (1996a): A probabilistic approach to feature selection—A filter solution. — Proc. 13th Int. Conf. Machine Learning (ICML’96), Bari, Italy, pp.319–327. 
26. Liu H. and Setiono R. (1996b): Feature selection and classification—A probabilistic wrapHUK per approach. — 9th Int. Conf. Industrial and Engineering Applications of Artificial Intelligence and Expert Systems (IEA-AIE’96), Fukuoka, Japan, pp.419–424. 
27. Liu H. and Motoda H. (1999): Feature Selection for Knowledge Discovery and Data Mining. — Dordrecht: Kluwer Academic Publishers. Zbl0908.68127
28. Lobo V., Moura-Pires F. and Świniarski R. (1997): Minimizing the number of neurons for a SOM-based classification, using Boolean function formalization. — Int. Rep., San Diego State University, Department of Mathematical and Computer Sciences. 
29. Marill T. and Green D.M. (1963): On the effectiveness of receptors in recognition systems. — IEEE Trans. Inf. Theory, Vol.9, pp.11–17. 
30. Modrzejewski M. (1993): Feature selection using rough sets theory. — Proc. European Conf. Machine Learning, pp.213–226. 
31. Narendra P.M. and Fukunaga K. (1977): A branch and bound algorithm for feature subset selection. — Trans. IEEE. Computers, Vol.C-26, pp.917–922. Zbl0363.68059
32. Nguyen T. et al. (1994): Application of rough sets, neural networks and maximum likelihood for texture classification based on singular value decomposition. — Proc. Int. Workshop RSSC Rough Sets and Soft Computing, San Jose, U.S.A., pp.332-339. 
33. Pal S.K. and Skowron A. (1999): Rough-Fuzzy Hybridization: A New Trend in Decision Making. — Singapore: Springer. Zbl0941.68129
34. Pawlak Z. (1982): Rough sets. — Int. J. Comp. Sci., Vol.11, pp.341–356. Zbl0501.68053
35. Pawlak Z. (1991): Rough Sets. Theoretical Aspects of Reasoning About Data. — Boston: Kluwer Academic Publishers. Zbl0758.68054
36. Pregenzer M. (1997): Distinction sensitive learning vector quantization. — Ph.D. Thesis, Graz University of Technology, Graz, Austria. 
37. Quinlan J.R. (1993): C4.5: Programs for Machine Learning. — New York: Morgan Kaufman. 
38. Rissanen J. (1978): Modeling by shortest data description. — Automatica, Vol.14, pp.465– 471. Zbl0418.93079
39. Samaria F. and Harter A. (1994): Parametrization of stochastic model for human face idenHUK tification. — Proc. IEEE Workshop Application of Computer Vision. 
40. Siedlecki W. and Sklanski J. (1988): On automatic feature selection. — Int. J. Pattern Recogn. Artif. Intell., Vol.2, No.2, pp.197–220. 
41. Skowron A. (1990): The rough sets theory and evidence theory. — Fundamenta Informaticae, Vol.13, pp.245–262. Zbl0752.94023
42. Swets D.L and Weng J.J. (1996): Using discriminant eigenfeatures for image retrieval. — IEEE Trans. Pattern Recogn. Mach. Intell., Vol.10, No.8, pp.831–836. 
43. Świniarski R. (1993): Introduction to rough sets, In: Materials of the Int. Short Course Neural Networks. Fuzzy and Rough Systems. Theory and Applications. — San Diego State University, San Diego, California, pp.1–24. 
44. Świniarski R. (1995): RoughFuzzyLab. — A software package developed at San Diego State University, San Diego, California. 
45. Świniarski R. and Nguyen J. (1996): Rough sets expert system for texture classification based on 2D spectral features. — Proc. 3rd Biennial European Joint Conf. Engineering Systems Design and Analysis ESDA’96, Montpellier, France, pp.3–8. 
46. Świniarski R., Hunt F., Chalret D. and Pearson D. (1995): Feature selection using rough sets and hidden layer expansion for rupture prediction in a highly automated production system. — Proc. 12th Int. Conf. Systems Science, Wrocław, Poland. 
47. Świniarski R. and Hargis L. (2001): Rough sets as a front and of neural networks texture classifiers. — Neurocomputing, Vol.36, pp.85–102. Zbl1003.68642
48. Swingler K. (1996): Applying Neural Networks. — London: Academic Press. 
49. Weiss S. and Indurkhya N. (1977): Predictive Data-Mining: A Practical Guide. — New York: Morgan Kaufmann. Zbl0885.68050
50. Yu B. and Yuan B. (1993): A more efficient branch and bound algorithm for feature selection. — Pattern Recognition, Vol.26, No.6, pp.883–889. 
51. Xu L., Yan P. and Chang T. (1989): Best first strategy for feature selection. — Proc. 9th Int. Conf. Pattern Recognition, pp.706–708. 

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