optimizing classifer
Optimizing Classifers for Imbalanced Training Sets
Following recent results [9, 8] showing the importance of the fat(cid:173) shattering dimension in explaining the beneficial effect of a large margin on generalization performance, the current paper investi(cid:173) gates the implications of these results for the case of imbalanced datasets and develops two approaches to setting the threshold. The approaches are incorporated into ThetaBoost, a boosting al(cid:173) gorithm for dealing with unequal loss functions. The performance of ThetaBoost and the two approaches are tested experimentally.
Optimizing Classifers for Imbalanced Training Sets
Karakoulas, Grigoris I., Shawe-Taylor, John
Following recent results [9, 8] showing the importance of the fatshattering dimension in explaining the beneficial effect of a large margin on generalization performance, the current paper investigates the implications of these results for the case of imbalanced datasets and develops two approaches to setting the threshold. The approaches are incorporated into ThetaBoost, a boosting algorithm for dealing with unequal loss functions. The performance of ThetaBoost and the two approaches are tested experimentally.
Optimizing Classifers for Imbalanced Training Sets
Karakoulas, Grigoris I., Shawe-Taylor, John
Following recent results [9, 8] showing the importance of the fatshattering dimension in explaining the beneficial effect of a large margin on generalization performance, the current paper investigates the implications of these results for the case of imbalanced datasets and develops two approaches to setting the threshold. The approaches are incorporated into ThetaBoost, a boosting algorithm for dealing with unequal loss functions. The performance of ThetaBoost and the two approaches are tested experimentally.
Optimizing Classifers for Imbalanced Training Sets
Karakoulas, Grigoris I., Shawe-Taylor, John
Following recent results [9, 8] showing the importance of the fatshattering dimensionin explaining the beneficial effect of a large margin on generalization performance, the current paper investigates theimplications of these results for the case of imbalanced datasets and develops two approaches to setting the threshold. The approaches are incorporated into ThetaBoost, a boosting algorithm fordealing with unequal loss functions. The performance of ThetaBoost and the two approaches are tested experimentally.