Publication
Title
Real-time discriminant analysis in the presence of label and measurement noise
Author
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.
Language
English
Source (journal)
Chemometrics and intelligent laboratory systems. - Amsterdam, 1986, currens
Publication
Amsterdam : 2021
ISSN
0169-7439 [print]
1873-3239 [online]
DOI
10.1016/J.CHEMOLAB.2020.104197
Volume/pages
208 (2021) , p. 1-12
Article Reference
104197
ISI
000609421000009
Full text (Publisher's DOI)
Full text (publisher's version - intranet only)
UAntwerpen
Research group
Publication type
Subject
External links
Web of Science
Record
Identifier
Creation 27.02.2024
Last edited 07.03.2024
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