Inducción de Árboles de Decisión basada en un Índice de Validación de Cluster
(Inducing Decision Trees based on a Cluster Quality Index)
Octavio Loyola-González (firstname.lastname@example.org)1, Miguel Angel Medina-Pérez (email@example.com)2, Milton García-Borroto (firstname.lastname@example.org)3
1Centro de Bioplantas2Instituto Tecnológico y de Estudios Superiores de Monterrey (ITESM-CEM)3Instituto Superior Politécnico “José Antonio Echeverría”
This paper appears in: Revista IEEE América Latina
Publication Date: April 2015
Volume: 13, Issue: 4
Decision trees are popular classifiers in data mining, artificial intelligence, and pattern recognition, because they are accurate and easy to comprehend. In this paper, we introduce a new procedure for inducing decision trees, to obtain trees that are more accurate, more compact, and more balanced. Each candidate split is evaluated using Rand Statistics, a quality index based on external measures, because it is considered by many authors as the best existing index. Our method was compared with other state-of-the-art methods and the results over 30 databases from the UCI Repository prove our claims. We also introduce a new equation to measure the balance of a binary tree.
supervised classification, decision trees, validation indexes, rand statistic, gain ratio, gini index
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