Honda, T., Motizuki, H., Ho, T.B., and Okumura, M. (1997). Generating Decision Trees from an Unbalanced Data Set. Poster papers presented at the 9th European Conference on Machine Learning (ECML), (pp. 68-77), edited by Maarten van Someren and Gerhard Widmer. Home/Search Document Not in Database Summary Related Articles Check |
This paper is cited in the following contexts:
Machine Learning for the Detection of Oil Spills in.. - Kubat, Holte, Matwin (1998) (22 citations) (Correct)
....the training set can be balanced by duplicating the training examples of the minority class or by creating new examples by corrupting existing ones with artificial noise (DeRouin et al. 1991) Solberg and Solberg (1996) do both: positives are duplicated and negatives are randomly sampled. Honda et al. 1997) reduce the imbalance by doing classification in two stages. In the first stage, the negatives most similar to the positives are included in the positive class. The second stage distinguishes these negatives from the true positives. This can be seen as a special case of multitask learning ....
Honda, T., Motizuki, H., Ho, T.B., and Okumura, M. (1997). Generating Decision Trees from an Unbalanced Data Set. Poster papers presented at the 9th European Conference on Machine Learning (ECML), (pp. 68-77), edited by Maarten van Someren and Gerhard Widmer.
Machine Learning for the Detection of Oil Spills in.. - Kubat, Holte, Matwin (1998) (22 citations) (Correct)
....the training set can be balanced by duplicating the training examples of the minority class or by creating new examples by corrupting existing ones with artificial noise (DeRouin et al. 1991) Solberg and Solberg (1996) do both; positives are duplicated and negatives are randomly sampled. Honda, Motizuki, Ho, and Okumura (1997) reduce the imbalance by doing classification in two stages. In the first stage, the negatives most similar to the positives are included in the positive class. The second stage distinguishes these negatives from the true positives. This can be seen as a special case of multitask learning ....
Honda, T., Motizuki, H., Ho, T.B., & Okumura, M. (1997). Generating Decision Trees from an Unbalanced Data Set. In van Someren, M., & Widmer, G. (Eds.), Poster papers presented at the 9th European Conference on Machine Learning (pp. 68-77).
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