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Label‐noise resistant logistic regression for functional data classification with an application to Alzheimer's disease study

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  • Seokho Lee
  • Hyejin Shin
  • Sang Han Lee

Abstract

Alzheimer's disease (AD) is usually diagnosed by clinicians through cognitive and functional performance test with a potential risk of misdiagnosis. Since the progression of AD is known to cause structural changes in the corpus callosum (CC), the CC thickness can be used as a functional covariate in AD classification problem for a diagnosis. However, misclassified class labels negatively impact the classification performance. Motivated by AD–CC association studies, we propose a logistic regression for functional data classification that is robust to misdiagnosis or label noise. Specifically, our logistic regression model is constructed by adopting individual intercepts to functional logistic regression model. This approach enables to indicate which observations are possibly mislabeled and also lead to a robust and efficient classifier. An effective algorithm using MM algorithm provides simple closed‐form update formulas. We test our method using synthetic datasets to demonstrate its superiority over an existing method, and apply it to differentiating patients with AD from healthy normals based on CC from MRI.

Suggested Citation

  • Seokho Lee & Hyejin Shin & Sang Han Lee, 2016. "Label‐noise resistant logistic regression for functional data classification with an application to Alzheimer's disease study," Biometrics, The International Biometric Society, vol. 72(4), pages 1325-1335, December.
    • Handle: RePEc:bla:biomet:v:72:y:2016:i:4:p:1325-1335
    DOI: 10.1111/biom.12504
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    References listed on IDEAS

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    1. She, Yiyuan & Owen, Art B., 2011. "Outlier Detection Using Nonconvex Penalized Regression," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 626-639.
    2. Shin, Hyejin, 2008. "An extension of Fisher's discriminant analysis for stochastic processes," Journal of Multivariate Analysis, Elsevier, vol. 99(6), pages 1191-1216, July.
    3. Lin, Yi, 2004. "A note on margin-based loss functions in classification," Statistics & Probability Letters, Elsevier, vol. 68(1), pages 73-82, June.
    4. de Leeuw, Jan, 2006. "Principal component analysis of binary data by iterated singular value decomposition," Computational Statistics & Data Analysis, Elsevier, vol. 50(1), pages 21-39, January.
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    Cited by:

    1. Yang, Seong J. & Shin, Hyejin & Lee, Sang Han & Lee, Seokho, 2020. "Functional linear regression model with randomly censored data: Predicting conversion time to Alzheimer ’s disease," Computational Statistics & Data Analysis, Elsevier, vol. 150(C).
    2. Samami, Maryam & Akbari, Ebrahim & Abdar, Moloud & Plawiak, Pawel & Nematzadeh, Hossein & Basiri, Mohammad Ehsan & Makarenkov, Vladimir, 2020. "A mixed solution-based high agreement filtering method for class noise detection in binary classification," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 553(C).

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