Real-time discriminant analysis in the presence of label and measurement noise

Iwein Vranckx, Jakob Raymaekers, Bart De ketelaere, Peter J. Rousseeuw*, Mia Hubert

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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Abstract

Quadratic discriminant analysis (QDA) is a widely used classification technique. Based on a training dataset, each class in the data is characterized by an estimate of its center and shape, which can then be used to assign unseen observations to one of the classes. The traditional QDA rule relies on the empirical mean and covariance matrix. Unfortunately, these estimators are sensitive to label and measurement noise which often impairs the model's predictive ability. Robust estimators of location and scatter are resistant to this type of contamination. However, they have a prohibitive computational cost for large scale industrial experiments. We present a novel QDA method based on a recent real-time robust algorithm. We additionally integrate an anomaly detection step to classify the most atypical observations into a separate class of outliers. Finally, we introduce the label bias plot, a graphical display to identify label and measurement noise in the training data. The performance of the proposed approach is illustrated in a simulation study with huge datasets, and on real datasets about diabetes and fruit.
Original languageEnglish
Pages (from-to)104197
Number of pages12
JournalChemometrics and Intelligent Laboratory Systems
Volume208
DOIs
Publication statusPublished - 15 Jan 2021
Externally publishedYes

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