TY - JOUR
T1 - A simple extension of boosting for asymmetric mislabeled data
AU - Hayashi, Kenichi
N1 - Funding Information:
This work is supported by Grant-in-Aid for JSPS Fellows from Japan Society for the Promotion of Science #21-302. The author would like to thank Bin Yu and her group for helpful discussions and comments. Finally, the author would like to acknowledge the associate editor and anonymous reviewers for their helpful comments and suggestions.
PY - 2012/2
Y1 - 2012/2
N2 - This letter provides a simple extension of boosting methods for binary data where the probability of mislabeling depends on the label of an example. Loss functions are derived from the statistical perspective, which is based on likelihood analysis. Our proposed methods can be interpreted as a correction of the decision boundary of observed labels. This interpretation partially relates to cost-sensitive learning, a classification method for the case in which the ratio of two labels in a dataset is skewed. Numerical experiments show that the proposed methods work well for asymmetric mislabeled data even when the probabilities of mislabeling may not be precisely specified.
AB - This letter provides a simple extension of boosting methods for binary data where the probability of mislabeling depends on the label of an example. Loss functions are derived from the statistical perspective, which is based on likelihood analysis. Our proposed methods can be interpreted as a correction of the decision boundary of observed labels. This interpretation partially relates to cost-sensitive learning, a classification method for the case in which the ratio of two labels in a dataset is skewed. Numerical experiments show that the proposed methods work well for asymmetric mislabeled data even when the probabilities of mislabeling may not be precisely specified.
KW - Asymmetric mislabeling mechanism
KW - Bayes error rate
KW - Boosting
KW - Classification
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U2 - 10.1016/j.spl.2011.10.014
DO - 10.1016/j.spl.2011.10.014
M3 - Article
AN - SCOPUS:81255166159
VL - 82
SP - 348
EP - 356
JO - Statistics and Probability Letters
JF - Statistics and Probability Letters
SN - 0167-7152
IS - 2
ER -