



@book {2299,
	title = {Decision trees using the minimum Entropy-of-error principle},
	series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci.},
	volume = {5702 LNCS},
	year = {2009},
	note = {Conference code: 77256Export Date: 21 November 2012Source: Scopusdoi: 10.1007/978-3-642-03767-2_97Language of Original Document: EnglishCorrespondence Address: De S{\'a}, J. P. M.; INEB-Instituto de Engenharia Biom{\'e}dica, Porto, Portugal; email: jmsa@fe.up.ptReferences: Asuncion, A., Newman, D.J., (2007) UCI Machine Learning Repository, , http://www.ics.uci.edu/̃mlearn/MLRepository.html, Univ. of California, SICS, Irvine, CA;Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J., (1993) Classification and Regression Trees, , Chapman \& Hall/CRC, Boca Raton; 
Devroye, L., Gi{\"o}rfi, L., Lugosi, G., (1996) A Probabilistic Theory of Pattern Recognition, , Springer, Heidelberg; 
Forina, M., Armanino, C., Eigenvector Projection and Simplified Nonlinear Mapping of Fatty Acid Content of Italian Olive Oils (1981) Ann. Chim, 72, pp. 127-155; 
Loh, W.-Y., Shih, Y.-S., Split Selection Methods for Classification Trees (1997) Statistica Sinica, 7, pp. 815-840; 
Marques de S{\'a}, J.P., (2007) Applied Statistics Using SPSS, STATISTICA, MATLAB and R, , 2nd edn. Springer, Heidelberg; 
Quinlan, J.R., Induction of Decision Trees (1986) Machine Learning, 1, pp. 81-106; 
Quinlan, J.R.: C4.5 Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)Silva, L.M., Felgueiras, C.S., Alexandre, L.A., Marques de S{\'a}, J., Error Entropy in Classification Problems: A Univariate Data Analysis (2006) Neural Comp, 18, pp. 2036-2061; 
Silva, L.M., Embrechts, M.J., Santos, J.M., de S{\'a}, J.M., The influence of the risk functional in data classification with mLPs (2008) LNCS, 5163, pp. 185-194. , K{\r u}rkov{\'a}, V, Neruda, R, Koutn{\'\i}k, J, eds, ICANN 2008, Part I, Springer, Heidelberg
Sponsors: University of Munster; International Association for Pattern Recognition; Olympus Soft Imaging Solutions GmbH; Philips},
	month = {2009///},
	pages = {799 - 807},
	address = {Munster},
	abstract = {Binary decision trees based on univariate splits have traditionally employed so-called impurity functions as a means of searching for the best node splits. Such functions use estimates of the class distributions. In the present paper we introduce a new concept to binary tree design: instead of working with the class distributions of the data we work directly with the distribution of the errors originated by the node splits. Concretely, we search for the best splits using a minimum entropy-of-error (MEE) strategy. This strategy has recently been applied in other areas (e.g. regression, clustering, blind source separation, neural network training) with success. We show that MEE trees are capable of producing good results with often simpler trees, have interesting generalization properties and in the many experiments we have performed they could be used without pruning. {\textcopyright} 2009 Springer Berlin Heidelberg.},
	keywords = {Binary decision trees, Binary trees, Blind source separation, Class distributions, decision trees, Electronic medical equipment, entropy, entropy-of-error, Generalization properties, Image analysis, Minimum entropy, Neural network training, Neural networks, New concept, node split criteria, Univariate},
	isbn = {03029743 (ISSN); 3642037666 (ISBN); 9783642037665 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-70349306674\&partnerID=40\&md5=39d60bd9d77f6ad61720d8ddd3f68e4d},
	author = {De S{\'a}, J.P.M. and Gama, J. and Sebasti{\~a}o, R. and Alexandre, L.A.}
}
