U.M. Fayyad and K.B. Irani. Multi-interval discretization of continous-valued attributes for classification learning. In R. Bajcsy, editor, Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, Chambery, France, pages 1022--1027, San Francisco, CA, 1993. Morgan Kaufmann. Home/Search Document Not in Database Summary Related Articles Check |
This paper is cited in the following contexts:
RainForest - A Framework for Fast Decision Tree.. - Gehrke, Ramakrishnan.. (1998) (26 citations) (Correct)
....to dealing with large databases. One approach is to discretize each ordered attribute and run the algorithm on the discretized data. But all discretization methods for classification that take the class label into account when discretizing assume that the database fits into main memory [Qui93, FI93, Maa94, DKS95] Catlett [Cat91] proposed sampling at each node of the classification tree, but considers in his studies only datasets that could fit in main memory. Methods for partitioning the dataset such that each subset fits in main memory are considered by Chan and Stolfo [CS93a, CS93b] ....
U.M. Fayyad and K. Irani. Multi-interval discretization of continous-valued attributes for classification learning. In Proc. of the International Joint Conference on Artificial Intelligence, 1993.
RainForest - A Framework for Fast Decision Tree.. - Gehrke, Ramakrishnan.. (1998) (26 citations) (Correct)
....to dealing with large databases. One approach is to discretize each ordered attribute and run the algorithm on the discretized data. But all discretization methods for classification that take the class label into account when discretizing assume that the database fits into main memory [Qui93, FI93, Maa94, DKS95] Catlett [Cat91] proposed sampling at each node of the classification tree, but considers in his studies only datasets that could fit in main memory. Methods for partitioning the dataset such that each subset fits in main memory are considered by Chan and Stolfo [CS93a, CS93b] ....
U.M. Fayyad and K. Irani. Multi-interval discretization of continous-valued attributes for classification learning. In Proc. of the International Joint Conference on Artificial Intelligence, 1993.
Feature Selection for High-Dimensional Data: A.. - Biesiada, Duch (Correct)
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U.M. Fayyad and K.B. Irani. Multi-interval discretization of continous-valued attributes for classification learning. In R. Bajcsy, editor, Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, Chambery, France, pages 1022--1027, San Francisco, CA, 1993. Morgan Kaufmann.
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