A Decision-Tree Framework for Instance-space Decomposition
This paper presents a novel instance-space decomposition framework for decision trees. According to this framework, the original instance-space is decomposed into several subspaces in a parallel-to-axis manner. A different classifier is assigned to each subspace. Subsequently, an unlabelled instance is classified by employing the appropriate classifier based on the subspace where the instance belongs. An experimental study which was conducted in order to compare various implementations of this framework indicates that previously presented implementations can be improved both in terms of accuracy and computation time.
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- 1.Dietterich, T.G. (1998). Approximate statistical tests for comparing supervised classification learning algorithms. Neural Computation, 10(7).Google Scholar
- 2.Esmeir S. and Markovitch S. (2004). Lookahead-based Algorithms for Anytime Induction of Decision Trees. In Proceedings of The Twenty-First International Conference on Machine Learning, Banff, Alberta, Canada. Morgan Kaufmann, pp. 257–264.Google Scholar
- 3.Kohavi R. (1996). Scaling Up the Accuracy of Naive-Bayes Classifiers: a Decision-Tree Hybrid, Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, pp. 202–207.Google Scholar
- 4.Maimon O. and Rokach L. (2005) Decomposition Methodology for Knowledge Discovery and Data Mining: Theory and Applications Series in Machine Perception and Artificial Intelligence — Vol. 61 World Scientific Publishing.Google Scholar
- 6.Murthy, S. and Salzberg, S. (1995), Lookahead and pathology in decision tree induction, in C. S. Mellish, ed., Proceedings of the 14th International Joint Conference on Artificial Intelligence, Morgan Kaufmann, pp. 1025–1031Google Scholar
- 7.Quinlan, J.R. (1993). C4.5: Programs for Machine Learning, Morgan Kaufmann.Google Scholar