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The Effect of Model Misspecification on Semi-Supervised Classification
October 2011 (vol. 33 no. 10)
pp. 2093-2103
C. E. Priebe, Dept. of Appl. Math. & Stat., Johns Hopkins Univ., Baltimore, MD, USA
Ting Yang, Dept. of Appl. Math. & Stat., Johns Hopkins Univ., Baltimore, MD, USA
Semi-supervised classification-training both on labeled and unlabeled observations-can yield improved performance compared to the classifier based on only the labeled observations. Unlabeled observations are always beneficial to classification if the model we assume is correct. However, they may degrade the classifier performance when the model is misspecified. In the classical classification problem setting, many factors affect the semi-supervised performance, including training data, model specification, estimation method, and the classifier itself. For concreteness, we consider maximum likelihood estimation in finite mixture models and the Bayes plug-in classifier, due to their ubiquitousness and tractability. In this specific setting, we examine the effect of model misspecification on semi-supervised classification performance and shed some light on when and why performance degradation occurs.

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Index Terms:
pattern classification,Bayes methods,learning (artificial intelligence),maximum likelihood estimation,performance degradation,unlabeled observations,classifier performance,training data,model specification,estimation method,maximum likelihood estimation,finite mixture model,Bayes plug-in classifier,model misspecification,semisupervised classification performance,Maximum likelihood estimation,Error analysis,Degradation,Biological system modeling,Parametric statistics,Estimation error,Bayes plug-in classifier.,Semi-supervised classification,finite mixture model
Citation:
C. E. Priebe, Ting Yang, "The Effect of Model Misspecification on Semi-Supervised Classification," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 10, pp. 2093-2103, Oct. 2011, doi:10.1109/TPAMI.2011.45
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