Bibliography - Data Mining with Decision Trees: Theory and Applications - page 276

Buchanan B. G., and Shortliffe E. H., Rule Based Expert Systems , pp. 272-292,
Addison-Wesley, 1984.
Buhlmann P., and Yu B., Boosting with L 2 loss: Regression and classification,
Journal of the American Statistical Association 98:324-338. 2003.
Buja A., and Lee Y. S., Data mining criteria for tree based regression and
classification, Proceedings of the 7th International Conference on Knowledge
Discovery and Data Mining , pp. 27-36, San Diego, USA, 2001.
Buntine W., A theory of learning classification rules, Doctoral Dissertation,
School of Computing Science, University of Technology, Sydney, Australia,
1990.
Buntine W., Learning classification trees, Statistics and Computing 2:63-73, 1992.
Buntine W., Graphical models for discovering knowledge, In Advances in
Knowledge Discovery and Data Mining , U. Fayyad, G. Piatetsky-Shapiro,
P. Smyth, and R. Uthurusamy (eds.), pp. 59-82, AAAI/MIT Press, 1996.
Buntine W., Niblett T., A further comparison of splitting rules for decision-tree
induction, Machine Learning 8:75-85, 1992.
Buczak A. L., and Ziarko W., Neural and rough set based data mining
methods in engineering, Handbook of Data Mining and Knowledge Discov-
ery , W. Klosgen and J. M. Zytkow (eds.), pp. 788-797, Oxford: Oxford
University Press, 2002a.
Buczak A. L., and Ziarko W., Stages of the discovery process, Handbook of Data
Mining and Knowledge Discovery ,W.KlosgenandJ.M.Zytkow(eds.),
pp. 185-192, Oxford: Oxford University Press, 2002b.
Can F. Incremental clustering for dynamic information processing, ACM Trans-
actions on Information Systems , No. 11, pp. 143-164, 1993.
Cantu-Paz E., and Kamath C., Inducing oblique decision trees with evolutionary
algorithms, IEEE Trans. on Evol. Computation 7(1):54-68, 2003.
Cardie C., Using decision trees to improve cased-based learning, Proceedings of the
First International Conference on Knowledge Discovery and Data Mining ,
AAAI Press, 1995.
Caruana R., Niculescu-Mizil A., Crew G., and Ksikes A., Ensemble selection
from libraries of models, Twenty-first International Conference on Machine
Learning , July 04-08, 2004, Banff, Alberta, Canada.
Carvalho D. R., and Freitas A. A., A hybrid decision-tree — genetic algorithm
method for data mining, Information Science 163:13-35, 2004.
Catlett J., Mega induction: Machine learning on vary large databases, PhD,
University of Sydney, 1991.
Chai B., Huang T., Zhuang X., Zhao Y., and Sklansky J., Piecewise-linear
classifiers using binary tree structure and genetic algorithm, Pattern
Recognition 29(11):1905-1917, 1996.
Chan P. K., and Stolfo S. J., Toward parallel and distributed learning by
meta-learning, In AAAI Workshop in Knowledge Discovery in Databases ,
pp. 227-240, 1993.
Chan P. K., and Stolfo S. J., A comparative evaluation of voting and meta-
learning on partitioned data, Proc. 12th Intl. Conf. On Machine Learning
ICML-95 , 1995.