



@book {2217,
	title = {New results on minimum error entropy decision trees},
	series = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Lect. Notes Comput. Sci.},
	volume = {7042 LNCS},
	year = {2011},
	note = {Conference code: 87418Export Date: 21 November 2012Source: Scopusdoi: 10.1007/978-3-642-25085-9_42Language of Original Document: EnglishCorrespondence Address: De S{\'a}, J.P.M.; INEB-Instituto de Engenharia Biom{\'e}dica, FEUP, Universidade Do Porto, Rua Dr. Roberto Frias, s/n, 4200-465 Porto, Portugal; email: jmsa@fe.up.ptReferences: Rokach, L., Maimon, O., Decision Trees (2005) Data Mining and Knowledge Discovery Handbook, , Maimon, O., Rokach, L. (eds.) Springer, Heidelberg;Marques De S{\'a}, J.P., Sebasti{\~a}o, R., Gama, J., Tree Classifiers Based on Minimum Error Entropy Decisions (2011) Can. J. Artif. Intell., Patt. Rec. and Mach. Learning, , in press; 
Silva, L., Felgueiras, C.S., Alexandre, L., Marques De S{\'a}, J., Error Entropy in Classification Problems: A Univariate Data Analysis (2006) Neural Computation, 18, pp. 2036-2061; 
Asuncion, A., Newman, D.J., (2010) UCI Machine Learning Repository, , http://www.ics.uci.edu/~mlearn/MLRepository.html; 
Kearns, M., A Bound on the Error of Cross Validation Using the Approximation and Estimation Rates, with Consequences for the Training-Test Split (1997) Neural Computation, 9, pp. 1143-1161; 
Molinaro, A.M., Simon, R., Pfeiffer, R.M., Prediction Error Estimation: A Comparison of Resampling Methods (2005) Bioinformatics, 21, pp. 3301-3307; 
Dem{\v s}ar, J., Statistical Comparisons of Classifiers over Multiple Data Sets (2006) J. of Machine Learning Research, 7, pp. 1-30; 
Garc{\'\i}a, S., Fern{\'a}ndez, A., Luengo, J., Herrera, F., Advanced Nonparametric Tests for Multiple Comparisons in the Design of Experiments in Computational Intelligence and Data Mining: Experimental Analysis of Power (2010) Information Sciences, 180, pp. 2044-2064; 
Hochberg, Y., Tamhane, A.C., (1987) Multiple Comparison Procedures, , John Wiley \& Sons, Inc; 
Salzberg, S.L., On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach (1997) Data Mining and Knowledge Discovery, 1, pp. 317-328; 
Jensen, D., Oates, T., Cohen, P.R., Building Simple Models: A Case Study with Decision Trees (1997) LECTURE NOTES IN COMPUTER SCIENCE, (1280), pp. 211-222. , Advances in Intelligent Data Analysis Reasoning about Data; 
Li, R.-H., Belford, G.G., Instability of Decision Tree Classification Algorithms (2002) Proc. 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 570-575
Sponsors: Universidad de La Frontera (UFRO); The International Association for Pattern Recognition (IAPR); Asociacon Chilena de Reconocimiento de Patrones (AChiRP); Asociacion Cubana de Reconocimiento de Patrones (ACPR); Mex. Assoc. Comput. Vis., Neural Comput. Rob. (MACVNR)},
	month = {2011///},
	pages = {355 - 362},
	address = {Pucon},
	abstract = {We present new results on the performance of Minimum Error Entropy (MEE) decision trees, which use a novel node split criterion. The results were obtained in a comparive study with popular alternative algorithms, on 42 real world datasets. Carefull validation and statistical methods were used. The evidence gathered from this body of results show that the error performance of MEE trees compares well with alternative algorithms. An important aspect to emphasize is that MEE trees generalize better on average without sacrifing error performance. {\textcopyright} 2011 Springer-Verlag.},
	keywords = {Algorithms, Alternative algorithms, Computer vision, Decision Theory, decision trees, entropy, entropy-of-error, Error entropy, Error performance, Forestry, New results, node split criteria, Real-world datasets, Vision},
	isbn = {03029743 (ISSN); 9783642250842 (ISBN)},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-81855161442\&partnerID=40\&md5=0d71ee4459a1a6a01536cfec0ff05664},
	author = {De S{\'a}, J.P.M. and Sebasti{\~a}o, R. and Gama, J. and Fontes, T.}
}
