Ordinal multi-classification methods have been studied widely. Traditional ordinal multi-classification methods assume that the sample label is not mislabeled. Due to the complexity of the real data and the limited artificial experience, it is unrealistic to obtain completely accurate labels, in which conventional methods perform poorly. In this article, we propose an ordinal mislabel logistic regression method based on γ-divergence, which possessing strong robustness when dealing with ordinal mislabeled response data. That is to say, when mislabeled, the weight of the sample in parameter estimation equation diminish compared to the case that the sample is properly labeled. Our method not only possesses the robustness but also can ignore the mislabel probabilities in the model. We construct the model by minimizing γ-divergence estimation and solve the model by gradient descent algorithm. Both simulation studies and real data analysis demonstrate that the method, namely robust ordinal mislabel logistic regression, is efficient to analyze ordinal mislabeled response data.
GUO Meijun
,
REN Mingyang
,
LI Shiming
,
ZHANG Sanguo
. Robust ordinal mislabel logistic regression based on γ-divergence[J]. Journal of University of Chinese Academy of Sciences, 2022
, 39(3)
: 289
-301
.
DOI: 10.7523/j.ucas.2020.0056
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