A simple extension of boosting for asymmetric mislabeled data

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3 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)348-356
Number of pages9
JournalStatistics and Probability Letters
Volume82
Issue number2
DOIs
Publication statusPublished - 2012 Feb

Keywords

  • Asymmetric mislabeling mechanism
  • Bayes error rate
  • Boosting
  • Classification

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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