Clasificación

Lecturas recomendadas
Sreerama K. Murthy: Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey. Data Mining and Knowledge Discovery 2(4):345-389 (1998). DOI 10.1023/A:1009744630224
Tjen-Sien Lim, Wei-Yin Loh, Yu-Shan Shih: A Comparison of Prediction Accuracy, Complexity, and Training Time of Thirty-Three Old and New Classification Algorithms. Machine Learning 40(3):203-228 (2000). DOI 10.1023/A:1007608224229
Janez Demsar: Statistical Comparisons of Classifiers over Multiple Data Sets. Journal of Machine Learning Research 7: 1-30 (2006)
David J. Hand: Classifier Technology and the Illusion of Progress. Statistical Science 21(1):1-14 (2006). DOI 10.1214/088342306000000060
Publicaciones de IDBIS
Fernando Berzal, Juan Carlos Cubero, Daniel Sánchez & José María Serrano: ART: A Hybrid Classification Model. Machine Learning, Volume 54, Number 1, Pages 67-92, January 2004. DOI B:MACH.0000008085.22487.a6
Fernando Berzal, Juan Carlos Cubero, Nicolás Marín & Daniel Sánchez: Building multi-way decision trees with numerical attributes. Information Sciences, Volume 165, pages 73-90, 2004. DOI 10.1016/j.ins.2003.09.018
Fernando Berzal, Juan Carlos Cubero, Fernando Cuenca & María José Martín-Bautista: On the quest for easy-to-understand splitting rules. Data & Knowledge Engineering, Vol. 44, No. 1, January 2003, pp. 31-48. DOI 10.1016/S0169-023X(02)00062-9
Fernando Berzal, Juan Carlos Cubero, Nicolás Marín & José Luis Polo: An overview of alternative rule evaluation criteria and their use in separate-and-conquer classifiers. ISMIS 2006, Foundations of Intelligent Systems, LNAI 4203, pp. 591-600. DOI 10.1007/11875604_66
José Luis Polo, Fernando Berzal & Juan Carlos Cubero: Taking class importance into account. ICHIT 2006, International Conference on Hybrid Information Technology, Cheju Island, Korea, November 9-11, 2006. DOI 10.1109/ICHIT.2006.233
José Luis Polo, Fernando Berzal & Juan Carlos Cubero: Class-Oriented Reduction of Decision Tree Complexity. ISMIS 2008, Foundations of Intelligent Systems, LNAI 4994, pp. 48-57. DOI 10.1007/978-3-540-68123-6_5