Bibliography - Data Mining with Decision Trees: Theory and Applications - page 279

Domingos P., Using partitioning to speed up specific-to-general rule induction,
In Proceedings of the AAAI-96 Workshop on Integrating Multiple Learned
Models , pp. 29-34, Portland, OR: AAAI Press, 1996.
Dominigos P., MetaCost: A general method for making classifiers cost sensitive. In
Proceedings of the Fifth International Conference on Knowledge Discovery
and Data Mining , pp. 155-164, ACM Press, 1999.
Domingos P., and Pazzani M., On the optimality of the naive bayes classifier
under zero-one loss, Machine Learning 29(2):103-130, 1997.
Dror M., Shabtai A., Rokach L., and Elovici Y., OCCT: A one-class clustering
tree for implementing one-to-many data linkage, IEEE Transactions on
Knowledge and Data Engineering , 2014.
Dougherty J., Kohavi R, and Sahami M., Supervised and unsupervised discretiza-
tion of continuous attributes, Machine Learning: Proceedings of the twelfth
International Conference , Morgan Kaufman, pp. 194-202, 1995.
Duda R., and Hart P., Pattern Classification and Scene Analysis ,New-York:
Wiley, 1973.
Duda P. E. H., and Stork D. G., Pattern Classification , New York: Wiley, 2001.
Dunteman G. H., Principal Components Analysis , Sage Publications, 1989.
Dzeroski S., and Zenko B., Is combining classifiers with stacking better than
selecting the best one? Machine Learning 54(3):255-273, 2004.
Elder I., and Pregibon D., A statistical perspective on knowledge discovery
in databases, In Advances in Knowledge Discovery and Data Mining ,
U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy (eds.),
pp. 83-113, Portland, OR: AAAI/MIT Press, 1996.
Esmeir S., and Markovitch S., Lookahead-basedalgorithms for anytime induction
of decision trees, InICML'04, 257-264, 2004.
Esposito F., Malerba D., and Semeraro G., A comparative analysis of methods
for pruning decision trees, IEEE Transactions on Pattern Analysis and
Machine Intelligence 19(5):476-492, 1997.
Ester M., Kriegel H. P., Sander S., and Xu X., A density-based algorithm for
discovering clusters in large spatial databases with noise, In Proceedings of
the 2nd International Conference on Knowledge Discovery and Data Mining
(KDD-96) , E. Simoudis, J. Han, and U. Fayyad, (eds.), pp. 226-231, Menlo
Park, CA, AAAI Press, 1996.
Estivill-Castro V., and Yang J., A Fast and robust general purpose clustering
algorithm, Pacific Rim International Conference on Artificial Intelligence ,
pp. 208-218, 2000.
Eyal A., Rokach L., Kalech M., Amir O., Chougule R., Vaidyanathan R., and
Pattada K., Survival analysis of automobile components using mutually
exclusive forests, IEEE Transactions Systems, Man, and Cybernetics , 2014.
Fayyad U., Grinstein G., and Wierse A., Information visualization in data mining
and knowledge discovery, Morgan Kaufmann, 2001.
Fayyad U., and Irani K. B., The attribute selection problem in decision tree
generation, In Proceedings of Tenth National Conference on Artificial
Intelligence , pp. 104-110, Cambridge, MA: AAAI Press/MIT Press, 1992.