In a practical classification problem, there are cases where incorrect labels are included in training data due to label noise. We introduce a classification method in the presence of label noise that idealizes a classification method based on the expectation-maximization (EM) algorithm, and evaluate its performance theoretically. Its performance is asymptotically evaluated by assessing the risk function defined as the Kullback-Leibler divergence between predictive distribution and true distribution. The result of this performance evaluation enables a theoretical evaluation of the most successful performance that the EM-based classification method may achieve.
Goki YASUDA
Waseda University
Tota SUKO
Waseda University
Manabu KOBAYASHI
Waseda University
Toshiyasu MATSUSHIMA
Waseda University
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Goki YASUDA, Tota SUKO, Manabu KOBAYASHI, Toshiyasu MATSUSHIMA, "Asymptotic Evaluation of Classification in the Presence of Label Noise" in IEICE TRANSACTIONS on Fundamentals,
vol. E106-A, no. 3, pp. 422-430, March 2023, doi: 10.1587/transfun.2022TAP0013.
Abstract: In a practical classification problem, there are cases where incorrect labels are included in training data due to label noise. We introduce a classification method in the presence of label noise that idealizes a classification method based on the expectation-maximization (EM) algorithm, and evaluate its performance theoretically. Its performance is asymptotically evaluated by assessing the risk function defined as the Kullback-Leibler divergence between predictive distribution and true distribution. The result of this performance evaluation enables a theoretical evaluation of the most successful performance that the EM-based classification method may achieve.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2022TAP0013/_p
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@ARTICLE{e106-a_3_422,
author={Goki YASUDA, Tota SUKO, Manabu KOBAYASHI, Toshiyasu MATSUSHIMA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Asymptotic Evaluation of Classification in the Presence of Label Noise},
year={2023},
volume={E106-A},
number={3},
pages={422-430},
abstract={In a practical classification problem, there are cases where incorrect labels are included in training data due to label noise. We introduce a classification method in the presence of label noise that idealizes a classification method based on the expectation-maximization (EM) algorithm, and evaluate its performance theoretically. Its performance is asymptotically evaluated by assessing the risk function defined as the Kullback-Leibler divergence between predictive distribution and true distribution. The result of this performance evaluation enables a theoretical evaluation of the most successful performance that the EM-based classification method may achieve.},
keywords={},
doi={10.1587/transfun.2022TAP0013},
ISSN={1745-1337},
month={March},}
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TY - JOUR
TI - Asymptotic Evaluation of Classification in the Presence of Label Noise
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 422
EP - 430
AU - Goki YASUDA
AU - Tota SUKO
AU - Manabu KOBAYASHI
AU - Toshiyasu MATSUSHIMA
PY - 2023
DO - 10.1587/transfun.2022TAP0013
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E106-A
IS - 3
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - March 2023
AB - In a practical classification problem, there are cases where incorrect labels are included in training data due to label noise. We introduce a classification method in the presence of label noise that idealizes a classification method based on the expectation-maximization (EM) algorithm, and evaluate its performance theoretically. Its performance is asymptotically evaluated by assessing the risk function defined as the Kullback-Leibler divergence between predictive distribution and true distribution. The result of this performance evaluation enables a theoretical evaluation of the most successful performance that the EM-based classification method may achieve.
ER -