Semi-Supervised Learning of Class Balance under Class-Prior Change by Distribution Matching (情報論的学習理論と機械学習)

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概要

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• In real-world classification problems, the class balance in the training dataset does not necessarily reflect that of the test dataset, which can cause significant estimation bias. If the class ratio of the test dataset is known, instance re-weighting or resampling allows systematical bias correction. However, learning the class ratio of the test dataset is challenging when no labeled data is available from the test domain. In this paper, we propose to estimate the class ratio in the test dataset by matching probability distributions of training and test input data. Our approach does not involve density estimation when distribution matching is carried out, and is computationally efficient. We demonstrate the utility of the proposed method through experiments.

• 一般社団法人電子情報通信学会の論文
• 2012-03-05

著者

• Du Plessis

  Department Of Botany And Genetics University Of The Orange Free State

• Sugiyama Masashi

  Department Of Chemistry Faculty Of Science Tokyo University Of Science

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