Feature Selection for Data and Pattern Recognition: An Introduction - Feature Selection for Data and Pattern Recognition - page 6

3. Blum, A., Langley, P.: Selection of relevant features and examples in machine learning. Artif.
Intell. 97 , 245-271 (1997)
4. Botsch, M.: Machine Learning Techniques for Time Series Classification. Cuvillier, San
Francisco (2009)
5. Breiman, L.: Random forests. Mach. Learn. 45 , 5-32 (2001)
6. Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees.
Wadsworth & Brooks, Monterey (1984)
7. Carbonell, J.G. (ed.): Machine Learning. Paradigms and Methods. MIT Press, Boston (1990)
8. Chao, L.L.: Introduction to Statistics. Brooks Cole Publishing Co., Monterey (1980)
9. Cipolla, R., Pentland, A.: Computer Vision for Human-Machine Interaction. Cambridge Uni-
versity Press, Cambridge (1998)
10. Dash, M., Liu, H.: Feature selection for classification. Intell. Data Anal. 1 , 131-156 (1997)
11. Duda, R.O., Hart, P.E.: Pattern Classification and Scene Analysis. Wiley, New York (1973)
12. Everitt, B.: Cluster Analysis. Heinemann Educational Books, London (1980)
13. Flusser, J., Zitova, B., Suk, T.: Moments and Moment Invariants in Pattern Recognition. Wiley,
New York (2009)
14. Fuernkranz, J., Gamberger, D., Lavrac, N.: Foundations of Rule Learning. Springer, Berlin
(2012)
15. Grzymala-Busse, J.W.: Knowledge acquisition under uncertainty—a rough set approach.
J. Intell. Robot. Syst. 1 , 3-16 (1988)
16. Grzymala-Busse, J.W.: Data with missing attribute values: generalization of indiscernibility
relation and rule induction. Trans. Rough Sets 1 , 78-95 (2004)
17. Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res.
3 , 1157-1182 (2003)
18. Guyon, I., Gunn, S., Nikravesh, M., Zadeh, L.A.: Feature Extraction. Foundations and Appli-
cations. Springer, Berlin (2006)
19. Hamburg, M.: Statistical Analysis for Decision Making. Harcourt Brace Jovanovich Inc., New
York (1983)
20. Hofstadter, D.: Metamagical Themas: Questing for the Essence of Mind and Pattern. Basic
Books Inc., New York (1985)
21. Holland, J.H., Holyoak, K.J., Nisbett, R.E.: Induction: Processes of Inference, Learning, and
Discovery. MIT Press, Boston (1986)
22. Hollander, M., Wolfe, D.A.: Nonparametric Statistical Methods, 2nd edn. Wiley, New York
(1999)
23. Jensen, R., Shen, Q.: Computational Intelligence and Feature Selection. Wiley, Hoboken (2008)
24. Kloesgen, W., Zytkow, J. (eds.): Handbook of Data Mining and Knowledge Discovery. Oxford
University Press, New York (2002)
25. Kohavi, R., John, G.: Wrappers for feature selection. Artif. Intell. 97 , 273-324 (1997)
26. Krawiec, K., Stefanowski, J.: Machine Learning and Neural Nets. Poznan University of Tech-
nology Press, Poznan (2003)
27. Kuncheva, L.I.: Combining Pattern Classifiers. Methods and Algorithms. Wiley, Hoboken
(2004)
28. Liu, H., Motoda, H.: Computational Methods of Feature Selection. Chapman and Hall/ CRC,
Boca Raton (2007)
29. Longman Dictionary of Contemporary English, 6th revised edn. Pearson Longman, London
(2014)
30. Maimon, O., Rokach, L. (eds.): Data Mining and Knowledge Discovery Handbook, 2nd edn.
Springer, Berlin (2010)
31. Meyers, R.A. (ed.): Encyclopedia of Complexity and Systems Science. Springer, Berlin (2009)
32. Pawlak, Z.: Rough Sets. Theoretical Aspects of Reasoning About Data. Kluwer Academic
Publishers, Boston (1991)
33. Polkowski, L., Skowron, A. (eds.): Rough Sets in Knowledge Discovery 1: Methodology and
Applications. Physica-Verlag, Heidelberg (1998)