Jun 22, 2011 · An empirical analysis of this algorithm shows that the class distribution of the resulting training set yields classifiers with good (nearly- ...
Learning When Training Data are Costly: The Effect of Class Distribution on Tree Induction 317limited to include approximately 650,000 training examples ...
An empirical analysis of this algorithm shows that the class distribution of the resulting training set yields classifiers with good (nearly-optimal) ...
When the training data must be limited due to the cost of learning from the data, then our re- sults—and the guidelines we establish—can help to determine the ...
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Oct 1, 2003 · An empirical analysis of this algorithm shows that the class distribution of the resulting training set yields classifiers with good (nearly- ...
A "budget-sensitive" progressive sampling algorithm is introduced for selecting training examples based on the class associated with each example and it is ...
In this article we analyze the relationship between the marginal class distribution of training data and the performance of classification trees induced from ...
Weiss, G. and Provost, F. (2003) Learning when Training Data Are ...
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Weiss, G. and Provost, F. (2003) Learning when Training Data Are Costly: The Effect of Class Distribution on Tree Induction. Journal of Artificial Intelligence ...
Jan 21, 2019 · Bibliographic details on Learning When Training Data are Costly: The Effect of Class Distribution on Tree Induction.
Learning when Training Data are Costly: The Effect of Class Distribution on Tree Induc- tion. Journal of Artificial Intelligence Research, 19 (2003),. 315 ...