G. Weiss and F. Provost. The effect of class distribution on classifier learning: An empirical study. Technical Report ML-TR 44, Rutgers University, 2001. Home/Search Document Details and Download
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Benefit Maximizing Classification Using Feature Intervals - Ikizler (2002) (Correct)
....there will be extensive shrink in this upper limit due to the reduction in number of intervals. Experimental Results When dealing with cost sensitivity, class imbalance in the datasets is the main issue faced with. Importance and consequences of this problem have been widely addressed in [48]. Most of the error based algorithms fail when the minority class is more valuable in the domain, and in some contexts, cost sensitive classification has become the process of detecting minority class. However, a generic method which is applicable to all sorts of domains is needed. That s why we ....
G. M. Weiss and F. Provost. The effect of class distribution on classifier learning. Technical Report ML-TR 43, Department of Computer Science, Rutgers University, 2001.
The Foundations of Cost-Sensitive Learning - Elkan (2001) (26 citations) (Correct)
....the actual values of these probabilities, we find that we have more variables to solve for than we have simultaneous equations. Fortunately, all we need to know for any particular example x is the ratio e. The special case of Theorem 2 where p 0 = 0:5 was recently worked out independently by Weiss and Provost [2001] . The case where b = 0:5 is also interesting. Suppose that we do not know the base rate of positive examples at the time we learn a classifier. Then it is reasonable to use a training set with b = 0:5. Theorem 2 says how to compute probabilities p 0 later that are correct given that the ....
Gary M. Weiss and Foster Provost. The effect of class distribution on classifier learning. Technical Report ML-TR 43, Department of Computer Science, Rutgers University, 2001.
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Weiss, G. M. & Provost, F. (2001). The effect of class distribution on classifier learning: an empirical study. Technical Report ML-TR-44, Department of Computer Science, Rutgers University, August 2001. An updated version has been submitted to the Journal of Artificial Intelligence Research (JAIR).
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G. Weiss and F. Provost. The effect of class distribution on classifier learning: An empirical study. Technical Report ML-TR 44, Rutgers University, 2001.
ON PREDICTING RARE CLASSES WITH SVM ENSEMBLES IN SCENE.. - School Of Computer (Correct)
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G.M. Weiss and F. Provost, "The effect of class distribution on classifier learning," Tech. Rep., Department of Computer Science, Rutgers University, 2001.
On Predicting Rare Classes With Svm Ensembles In Scene.. - Yan, Liu, Jin, Hauptmann (2003) (1 citation) (Correct)
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G.M. Weiss and F. Provost, "The effect of class distribution on classifier learning," Tech. Rep., Department of Computer Science, Rutgers University, 2001.
On Predicting Rare Classes With Svm Ensembles In Scene.. - Yan, Liu, Jin, Hauptmann (2003) (1 citation) (Correct)
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G.M. Weiss and F. Provost, "The effect of class distribution on classifier learning," Tech. Rep., Department of Computer Science, Rutgers University, 2001.
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Weiss, G. & Provost, F. (2001) The Effect of Class Distribution on Classifier Learning: An Empirical Study, Technical Report ML-TR-44, Department of Computer Science, Rutgers University.
Volume Under the ROC Surface for Multi-class.. - Ferri.. (2003) (Correct)
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Weiss, G. and Provost, F. "The Effect of Class Distribution on Classifier Learning: An Empirical Study" Technical Report ML-TR-44, Department of Computer Science, Rutgers University, 2001.
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