Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning
Citations
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20,196 citations
10,306 citations
Cites methods from "Multi-Interval Discretization of Co..."
...5), naive Bayesian learner that models continuous probabilities using LOESS (Cleveland, 1979), naive Bayesian learner with continuous attributes discretized using Fayyad-Irani’s discretization (Fayyad and Irani, 1993) and kNN (k=10, neighbour weights adjusted with the Gaussian kernel)....
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...…original C4.5), naive Bayesian learnerthat models continuous probabilities using LOESS (Cleveland, 1979), naive Bayesian learner with continuous attributes discretized using Fayyad-Irani’s discretization (Fayyad and Irani, 1993) and kNN (k=10, neighbour weights adjusted with the Gaussian kernel)....
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4,775 citations
Cites background or methods from "Multi-Interval Discretization of Co..."
...This pre-discretization is based on a variant of Fayyad and Irani’s (1993) discretization method....
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..., 1995), and the function based on the principle of minimal description length(MDL) (Lam & Bacchus, 1994; Suzuki, 1993); see also Friedman and Goldszmidt (1996c) for a more recent account of this scoring function....
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...This is done using a discretization procedure such as the one suggested by Fayyad and Irani (1993), to partition the range of each numerical attribute....
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3,533 citations
References
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