This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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356
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Type:
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Contributed
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Date/Time:
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Tuesday, August 3, 2010 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistical Learning and Data Mining
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Abstract - #307800 |
Title:
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A Boosting Method with Asymmetric Mislabeling Probabilities That Depend on Covariates
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Author(s):
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Kenichi Hayashi*+
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Companies:
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Osaka University
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Address:
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, , ,
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Keywords:
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Boosting ;
Classification ;
Asymmetric mislabeling mechanism ;
Robustness
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Abstract:
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In this paper, we provide a boosting method for a kind of noisy data where the probability of mislabeling depends on the label of a case. The mechanism of the model is based on a simple idea and gives natural interpretation as a mislabel model. The boosting algorithm is derived from an extension of the exponential loss function, which gives the AdaBoost algorithm. The connection between the proposed method and an asymmetric mislabel model is shown. We show that the loss function proposed constructs a classifier which attains the minimum error rate for a true label. Numerical experiments illustrate how well the proposed method performs in comparison to existing methods.
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The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.
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