Selecting feature subset for high dimensional data via the propositional FOIL rules

Pattern Recognition

Volumn 46, Issue 1, 2013, Pages 199-214

  Wang, Guangtao ^a   Song, Qinbao ^a   Xu, Baowen ^b   Zhou, Yuming ^b  

^a XI'AN JIAOTONG UNIVERSITY   (China)

^b NANJING UNIVERSITY   (China)

Author keywords

Feature interaction; Feature subset selection; Filter method; Propositional FOIL rule

Indexed keywords

CLASSIFICATION ACCURACY; FEATURE INTERACTIONS; FEATURE SELECTION ALGORITHM; FEATURE SPACE; FEATURE SUBSET; FEATURE SUBSET SELECTION; FILTER METHOD; HIGH DIMENSIONAL DATA; NAIVE BAYES; PROPOSITIONAL FOIL RULE; REAL WORLD DATA; REDUNDANT FEATURES; RULE BASED; RULE BASED ALGORITHMS; RUNTIMES; SYNTHETIC DATASETS;

ALGORITHMS; CLASSIFICATION (OF INFORMATION); DATA MINING; SET THEORY;

FEATURE EXTRACTION;

EID: 84866031290     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2012.07.028     Document Type: Article

References (46)

1. L.C. Molina, L. Belanche, À. Nebot, Feature selection algorithms: a survey and experimental evaluation, in: Proceedings of IEEE International Conference on Data Mining, IEEE Computer Society, 2002, pp. 306-313.
2. G.H. John, R. Kohavi, K. Pfleger, Irrelevant features and the subset selection problem, in: Proceedings of the Eleventh International Conference on Machine Learning, 1994.
3. L. Yu, H. Liu, Redundancy based feature selection for microarray data, in: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, 2004, pp. 737-742.
4. G. Forman An extensive empirical study of feature selection metrics for text classification Journal of Machine Learning Research 3 2003 1289 1305
5. K. Kira, L.A. Rendell, The feature selection problem: traditional methods and a new algorithm, in: Proceedings of the National Conference on Artificial Intelligence, American Association for Artificial, 1992, pp. 129-134.
6. I. Kononenko, Estimating attributes: analysis and extensions of RELIEF, in: Proceedings of the 1994 European Conference on Machine Learning, Springer, 1994, pp. 171-182.
7. H. Park, H.C. Kwon, Extended relief algorithms in instance-based feature filtering, in: Proceedings of the Sixth International Conference on Advanced Language Processing and Web Information Technology, IEEE Computer Society, 2007, pp. 123-128.
8. M. Scherf, W. Brauer, Feature Selection by Means of a Feature Weighting Approach, Technical Report, Institut fur Informatik, Technische Universitat Munchen, 1997.
9. R. Battiti Using mutual information for selecting features in supervised neural net learning IEEE Transactions on Neural Networks 5 4 1994 537 550
10. M.A. Hall, Correlation-based feature selection for discrete and numeric class machine learning, in: Proceedings of the Seventeenth International Conference on Machine Learning, 2000, pp. 359-366.
11. H. Liu, R. Setiono, A probabilistic approach to feature selection-a filter solution, in: Proceedings of the 13th International Conference on Machine Learning, 1996, pp. 319-327.
12. M. Modrzejewski, Feature selection using rough sets theory, in: Proceedings of the European Conference on Machine Learning, Springer, 1993, pp. 213-226.
13. L. Yu, H. Liu, Feature selection for high-dimensional data: a fast correlation-based filter solution, in: Proceedings of 20th International Conference on Machine Learning, vol. 20, 2003, pp. 856-863.
14. Q. Song, J. Ni, and G. Wang A fast clustering-based feature subset selection algorithm for high dimensional data IEEE Transactions on Knowledge and Data Engineering 99 2011 1 14
15. A. Jakulin, I. Bratko, Analyzing attribute dependencies, in: Proceedings of Seventh European Conference on Principles and Practice of Knowledge Discovery in Databases, Springer, 2003, pp. 229-240.
16. A. Jakulin, I. Bratko, Testing the significance of attribute interactions, in: Proceedings of the Twenty-First International Conference on Machine Learning, ACM, 2004, pp. 409-416.
17. Z. Zhao, and H. Liu Searching for interacting features in subset selection Intelligent Data Analysis 13 2 2009 207 228
18. J. Xie, J. Wu, Q. Qian, Feature selection algorithm based on association rules mining method, in: 2009 Eighth IEEE/ACIS International Conference on Computer and Information Science, IEEE, 2009, pp. 357-362.
19. R. Agrawal, R. Srikant, Fast algorithms for mining association rules, in: Proceedings 20th International Conference on Very Large Data Bases, vol. 1215, 1994, pp. 487-499.
20. J.R. Quinlan, R.M. Cameron-Jones, FOIL: a midterm report, in: Proceedings of the European Conference on Machine Learning, Springer, 1993, pp. 1-20.
21. W.W. Cohen, Fast effective rule induction, in: Proceedings of the Twelfth International Conference on Machine Learning, Morgan Kaufmann, 1995, pp. 115-123.
22. X. Yin, J. Han, CPAR: classification based on predictive association rules, in: Proceedings of the Third SIAM International Conference on Data Mining, Society for Industrial & Applied, 2003, pp. 331-335.
23. M. Dash, and H. Liu Feature selection for classification Intelligent Data Analysis 1 3 1997 131 156
24. J. Souza, Feature Selection with a General Hybrid Algorithm, Ph.D. Thesis, University of Ottawa, 2005.
25. A.L. Blum, and P. Langley Selection of relevant features and examples in machine learning Artificial Intelligence 97 1-2 1997 245 271
26. I. Guyon, and A. Elisseeff An introduction to variable and feature selection Journal of Machine Learning Research 3 2003 1157 1182
27. D. Koller, M. Sahami, Toward optimal feature selection, in: Proceedings of International Conference on Machine Learning, 1996, pp. 284-292.
28. M. ElAlami A filter model for feature subset selection based on genetic algorithm Knowledge-Based Systems 22 5 2009 356 362
29. R. Kohavi, and G.H. John Wrappers for feature subset selection Artificial Intelligence 97 1-2 1997 273 324
30. M.A. Hall, Correlation-Based Feature Selection for Machine Learning, Ph.D. Thesis, University of Waikato, 1999.
31. L. Yu, and H. Liu Efficient feature selection via analysis of relevance and redundancy Journal of Machine Learning Research 5 2004 1205 1224
32. F. Fleuret Fast binary feature selection with conditional mutual information Journal of Machine Learning Research 5 2004 1531 1555
33. I.A. Gheyas, and L.S. Smith Feature subset selection in large dimensionality domains Pattern Recognition 43 1 2010 5 13
34. P. Chanda, Y.R. Cho, A. Zhang, M. Ramanathan, Mining of attribute interactions using information theoretic metrics, in: Proceedings of the 2009 IEEE International Conference on Data Mining Workshops, IEEE Computer Society, 2009, pp. 350-355.
35. A. Asuncion, D. Newman, UCI machine learning repository, 2007.
36. U. Fayyad, K. Irani, Multi-interval discretization of continuous-valued attributes for classification learning, in: Proceedings of Thirteenth International Joint Conference on Artificial Intelligence, 1993, pp. 1022-1027.
37. G.H. John, P. Langley, Estimating continuous distributions in Bayesian classifiers, in: Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, vol. 1, Citeseer, 1995, pp. 338-345.
38. J.R. Quinlan C4.5: Programs for Machine Learning 1993 Morgan Kaufmann
39. E. Frank, I.H. Witten, Generating accurate rule sets without global optimization, in: Proceedings of the Fifteenth International Conference on Machine Learning, 1998, pp. 144-151.
40. D.W. Aha, D. Kibler, and M.K. Albert Instance-based learning algorithms Machine Learning 6 1 1991 37 66
41. R. Kohavi, A study of cross-validation and bootstrap for accuracy estimation and model selection, in: International Joint Conference on Artificial Intelligence, vol. 14, Citeseer, 1995, pp. 1137-1145.
42. M. Friedman A comparison of alternative tests of significance for the problem of m rankings The Annals of Mathematical Statistics 11 1 1940 86 92
43. S. Holm A simple sequentially rejective multiple test procedure Journal of Statistics 6 2 1979 65 70
44. J. Demšar Statistical comparisons of classifiers over multiple data sets Journal of Machine Learning Research 7 2006 1 30
45. S. García, and F. Herrera An extension on statistical comparisons of classifiers over multiple data sets for all pairwise comparisons Journal of Machine Learning Research 9 2008 2677 2694
46. I. Rish, J. Hellerstein, T. Jayram, An analysis of data characteristics that affect naive Bayes performance, in: Proceedings of the Eighteenth Conference on Machine Learning, Morgan Kaufmann, 2001.