Feature Evaluation by Filter, Wrapper, and Embedded Approaches - Feature Selection for Data and Pattern Recognition - page 41

References
1. Ahonen, H., Heinonen, O., Klemettinen, M., Verkamo, A.: Applying data mining techniques
in text analysis. Technical Report C-1997-23. Department of Computer Science, University of
Helsinki, Finland (1997)
2. Argamon, S., Karlgren, J., Shanahan, J.: Stylistic analysis of text for information access. In:
Proceedings of the 28th International ACM Conference on Research and Development in
Information Retrieval, Brazil (2005)
3. Argamon, S., Burns, K., Dubnov, S. (eds.): The Structure of Style: Algorithmic Approaches to
Understanding Manner and Meaning. Springer, Berlin (2010)
4. Baayen, H., van Haltern, H., Tweedie, F.: Outside the cave of shadows: using syntactic anno-
tation to enhance authorship attribution. Lit. Linguist. Comput. 11 (3), 121-132 (1996)
5. Bayardo Jr, R., Agrawal, R.: Mining the most interesting rules. In: Proceedings of the 5th ACM
SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 145-154
(1999)
6. Berber Sardinha, T.: Using key words in text analysis: practical aspects. Available on-line from
ftp://ftp.liv.ac.uk/pub/linguistics (1999)
7. Craig, H.: Stylistic analysis and authorship studies. In: Schreibman, S., Siemens, R., Unsworth,
J. (eds.) A Companion to Digital Humanities. Blackwell, Oxford (2004)
8. Dash, M., Liu, H.: Feature selection for classification. Intell. Data Anal. 1 , 131-156 (1997)
9. Dash, M., Liu, H.: Consistency-based search in feature selection. Artif. Intell. 151 , 155-176
(2003)
10. Deuntsch, I., Gediga, G.: Rough Set Data Analysis: A Road to Noninvasive Knowledge
Discovery. Mathodos Publishers, Bangor (2000)
11. Fiesler, E., Beale, R.: Handbook of Neural Computation. Oxford University Press, Oxford
(1997)
12. Greco, S., Matarazzo, B., Słowi nski, R.: Rough set theory for multicriteria decision analysis.
Eur. J. Oper. Res. 129 (1), 1-47 (2001)
13. Greco, S., Matarazzo, B., Słowi nski, R.: Dominance-based rough set approach as a proper way
of handling graduality in rough set theory. Trans. Rough Sets 7 , 36-52 (2007)
14. Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res.
3 , 1157-1182 (2003)
15. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.: The WEKA data
mining software: an update. SIGKDD Explor. 11 (1), 10-18 (2009)
16. Jelonek, J., Krawiec, K., Stefanowski, J.: Comparative study of feature subset selection tech-
niques for machine learning tasks. In: Proceedings of the 7th Workshop on Intelligent Infor-
mation Systems (1998)
17. Jensen, R., Shen, Q.: Computational Intelligence and Feature Selection. Wiley, Hoboken (2008)
18. John, G., Kohavi, R., Pfleger, K.: Irrelevant features and the subset selection problem. In: Cohen,
W., Hirsh, H. (eds.) Machine Learning: Proceedings of the 11th International Conference, pp.
121-129. Morgan Kaufmann Publishers (1994)
19. Kavzoglu, T., Mather, P.: Assessing artificial neural network pruning algorithms. In: Proceed-
ings of the 24thAnnual Conference and Exhibition of the Remote Sensing Society, pp. 603-609.
Greenwich (2011)
20. Khmelev, D., Tweedie, F.: Using Markov chains for identification of writers. Lit. Linguist.
Comput. 16 (4), 299-307 (2001)
21. Kingston, G., Maier, H., Lambert, M.: A statistical input pruning method for artificial neural
networks used in environmental modelling. In: Transactions of the 2nd Biennial Meeting of
the International Environmental Modelling and Software Society, pp. 87-92. Osnabrueck,
Germany (2004)
22. Kohavi, R., John, G.: Wrappers for feature subset selection. Artif. Intell. 97 (1-2), 273-324
(1997)