Title :
Data mining with ensembles of fuzzy decision trees
Author :
Marsala, Christophe
Author_Institution :
LIP6, Univ. Pierre et Marie Curie Paris 6, Paris
fDate :
March 30 2009-April 2 2009
Abstract :
In this paper, a study is presented to explore ensembles of fuzzy decision trees. First of all, a quick recall of the state of the art related to ensembles of (fuzzy) decision trees in Machine Learning is presented. Afterwards, a new approach to construct a forest of fuzzy decision trees is proposed. Two experiments are described, one with forests of fuzzy decision trees, and the other with bagging of fuzzy decision trees. The results highlight the interest of using fuzzy set theory in this kind of approaches.
Keywords :
data mining; decision trees; fuzzy set theory; learning (artificial intelligence); data mining; fuzzy decision trees; machine learning; Bagging; Boosting; Classification tree analysis; Data mining; Decision trees; Error analysis; Fuzzy set theory; Machine learning; Machine learning algorithms; Probability;
Conference_Titel :
Computational Intelligence and Data Mining, 2009. CIDM '09. IEEE Symposium on
Conference_Location :
Nashville, TN
Print_ISBN :
978-1-4244-2765-9
DOI :
10.1109/CIDM.2009.4938670
