A boosting method with asymmetric mislabeling probabilities which depend on covariates

研究成果: Article査読

5 被引用数 (Scopus)

抄録

A new boosting method for a kind of noisy data is developed, 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 provides the AdaBoost algorithm. A connection between the proposed method and an asymmetric mislabel model is shown. It is also shown 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.

本文言語English
ページ(範囲)203-218
ページ数16
ジャーナルComputational Statistics
27
2
DOI
出版ステータスPublished - 2012 6
外部発表はい

ASJC Scopus subject areas

  • 統計学および確率
  • 統計学、確率および不確実性
  • 計算数学

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