Producción Científica Profesorado

Multivariate Decision Trees Using Different Splitting Attribute Subsets for Large Datasets



Franco Arcega, Anilú

2010

Franco-Arcega A., Carrasco-Ochoa J.A., Sánchez-Díaz G. y Martínez-Trinidad J.Fco. Multivariate decision tres using different splitting attribute subsets for large datasets. In Proc. of the 23th Canadian Conference on Artificial Intelligence. Lecture Notes in Computer Sciences 6085, ISSN: 0302-9743, pp. 370-373, 2010


Abstract


In this paper, we introduce an incremental induction of multivariate decision tree algorithm, called IIMDTS, which allows choosing a different splitting attribute subset in each internal node of the decision tree and it processes large datasets. IIMDTS uses all instances of the training set for building the decision tree without storing the whole training set in memory. Experimental results show that our algorithm is faster than three of the most recent algorithms for building decision trees for large datasets.



Producto de Investigación




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Multivariate Decision Trees Using Different Splitting Attribute Subsets for Large Datasets