Unsupervised discretization using kernel density estimation

IJCAI International Joint Conference on Artificial Intelligence

Volumn , Issue , 2007, Pages 696-701

  Biba, Marenglen ^a   Esposito, Floriana ^a   Ferilli, Stefano ^a   Di Mauro, Nicola ^a   Basile, Teresa M A ^a  

^a UNIVERSITY OF BARI   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

CONTINUOUS ATTRIBUTE; CONTINUOUS FEATURES; DISCRETIZATION METHOD; KERNEL DENSITY ESTIMATION; KERNEL DENSITY ESTIMATORS; NON-PARAMETRIC DENSITY ESTIMATORS; SUPERVISED DISCRETIZATION; UNSUPERVISED DISCRETIZATION;

ARTIFICIAL INTELLIGENCE; LEARNING ALGORITHMS; VOLUME MEASUREMENT;

DISCRETE EVENT SIMULATION;

EID: 84880873970     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (6)

1. Cerquides. J and Mantaras R.L. Proposal and empirical comparison of a parallelizable distance-based discretization method. In KDD97. Third International Conference on Knowledge Discovery and Data Mining, pp. 139-142.
2. Chmielevski, M.R and Grzymala-Busse,J.W. Global discretization of continuous attributes on preprocessing for machine learning. In Third International Workshop on Rough Sets and Soft Computing, pp. 294-301, 1994.
3. Dougherty.J.,Kohavi,R., and Sahami, M. Supervised and unsupervised discretization discretization of continuous features. In Proc. Twelfth International Conference on Machine Learning, Los Altos, CA:Morgan Kaufman,pp 194-202, 1995.
4. Greengard, L. and Strain, J.The fast Gauss Transform. SIAM Journal of Scientific and statistical computing. 12, 1, 79-94.
5. Silverman, B.W. Density estimation for statistics and data analysis. Chapman and Hall, London, 1986.
6. Yang, C., Duraiswami, R., and Gumerov, N. 2003. Improved fast Gauss transform. Tech. Rep.CS-TR-4495, Dept. of Computer Science, University of Maryland, College Park.