Skip to Main content Skip to Navigation
Journal articles

Parametric classification with soft labels using the Evidential EM algorithm : linear discriminant analysis versus logistic regression

Abstract : Partially supervised learning extends both supervised and unsu-pervised learning, by considering situations in which only partial information about the response variable is available. In this paper, we consider partially supervised classification and we assume the learning instances to be labeled by Dempster-Shafer mass functions, called soft labels. Linear discriminant analysis and logistic regression are considered as special cases of generative and discriminative parametric models. We show that the evidential EM algorithm can be particularized to fit the parameters in each of these models. We describe experimental results with simulated data sets as well as with two real applications: K-complex detection in sleep EEGs signals and facial expression recognition. These results confirm the interest of using soft labels for classification as compared to potentially erroneous crisp labels, when the true class membership is partially unknown or ill-defined.
Complete list of metadatas

Cited literature [48 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-01525605
Contributor : Thierry Denoeux <>
Submitted on : Thursday, July 5, 2018 - 5:57:33 AM
Last modification on : Tuesday, May 28, 2019 - 4:08:10 PM
Long-term archiving on: : Monday, October 1, 2018 - 4:25:34 PM

File

adac2017_v3.pdf
Files produced by the author(s)

Identifiers

Collections

Citation

Benjamin Quost, Thierry Denoeux, Shoumei Li. Parametric classification with soft labels using the Evidential EM algorithm : linear discriminant analysis versus logistic regression. Advances in Data Analysis and Classification, Springer Verlag, 2017, 11 (4), pp.659-690. ⟨10.1007/s11634-017-0301-2⟩. ⟨hal-01525605v2⟩

Share

Metrics

Record views

627

Files downloads

367