1 |
Witten, I. H. and Frank, E. (2000). Data Mining Practical Machine learning Tools and Techniques, Morgan kaufmann. Available from: http://www.cs.waikato.ac.nz/ml/weka/
|
2 |
Zhaoa, Y. H. and Zhang, Y. (2008). Comparison of decision tree methods for finding active objects, Advances in Space Research, 41, 1955-1959.
DOI
ScienceOn
|
3 |
Ziarko, W. (1993). Variable precision rough set model, Journal of Computer and System Sciences, 46, 39-59.
DOI
ScienceOn
|
4 |
Liu, H. and Setiono, R. (1995). Chi2: Feature selection and discretization of numeric attributes, Tools with Artificial Intelligence, 388–391.
|
5 |
Liu, H. and Setiono, R. (1997). Feature selection and discretization, IEEE Transactions on Knowledge and Data Engineering, 9, 642-645.
DOI
ScienceOn
|
6 |
Quinlan, R. (1993). C4.5: Programs for Machine Learning, Morgan Kaufmann Publishers Inc., San Francisco.
|
7 |
Liu, H., Hussain, H. F., Tan, C. L. and Dash, M. (2002). Discretization : An enabling technique, Data Mining and Knowledge Discovery, 6, 393-423.
DOI
ScienceOn
|
8 |
Merz, C. J. and Murphy, P. M. (1998). UCI repository of machine learning database, department of information and computer science, University of California, Irvine, California, Available from: http://www.ics.uci.edu/ mlearn/MLRepository.html
|
9 |
Pawlak, Z. (1982). Rough sets, International Journal of Computer and Information Sciences, 11, 341-356.
DOI
|
10 |
R Development Core Team (2005). R: A language and environment for statistical computing, R Foundation for statistical computing, Vienna, Austria, ISBN 3-900051-07-0, URL http://www.R-project.org.
|
11 |
Sotiris, K. and Dimitris, K. (2006). Discretization techiniques: A recent survey, GESTES International Transactions on Computer Science and Engineering, 32, 47-58.
|
12 |
Su, C. T. and Hsu, J. H. (2005). An extended Chi2 algorithm for discretization of real value attributes, IEEE Transactions on Knowledge and Data Engineering, 17, 437–441.
DOI
|
13 |
Tay, F. E. H. and Shen, L. (2002). Modified Chi2 algorithm for discretization, IEEE Transactions on Knowledge and Data Engineering, 14, 666-670.
DOI
ScienceOn
|
14 |
Tsai, C. J., Lee, C. I. and Yang, W. P. (2008). A discretization algorithm based on class-attribute contingency coefficient, Information Sciences, 178, 714-731.
DOI
ScienceOn
|
15 |
Fayyad, U. M. and Irani, K. B. (1993). Multi-interval discretization of continuous-valued attributes for classification learning, Artificial Intelligence, 13, 1022-1027.
|
16 |
Acuna, E. (2005). Dprep: Data preprocessing and visualization functions for classification, R package version 1.0. http://paginas.fe.up.pt/˜ec/files 0506/R/dprep.pdf.
|
17 |
Chmielewski, M. R. and Grzymala-Busse, J. W. (1996). Global discretization of continuous attributes as preprocessing for machine learning, International Journal of Approximate Reasoning, 15, 319-331.
DOI
ScienceOn
|
18 |
Dougherty, J., Kohavi, R. and Sahami, M. (1995). Supervised and unsupervised discretization of continuous features, Machine learning, 194-202.
|
19 |
Gonzalez-Abril, L., Cuberos, F. J., Velasco, F. and Ortega, J. A. (2009). Ameva: An autonomous discretization algorithm, Expert Systems with Applications, 36, 5327–5332.
|
20 |
Jin, H. and Charles, L. (2005). Using AUC and accuracy in evaluating learning algorithms, IEEE Transactions on Knowledge and Data Engineering, 17, 299-310.
DOI
ScienceOn
|
21 |
Kerber, R. (1992). ChiMerge: Discretization of numeric attributes, In Proceedings of the Tenth National Conference on Artificial Intelligence, 123-128.
|
22 |
Kim, H. J. (2010). Discretization: Data preprocessing, discretization for classification. R package version 1.0. http://lib.stat.cmu.edu/R/CRAN/web/packages/discretization/index.html.
|
23 |
Kurgan, L. A. and Cios, K. J. (2004). CAIM discretization algorithm, IEEE Transactions on Knowledge and Data Engineering, 16, 145-153.
|
24 |
Ling, C. X., Huang, J. and Zhang, H. (2003). AUC : A better measure than accuracy in comparing learning algorithm, Advances in Artificial Intelligence, 2671, 991.
|