Skip to Main content Skip to Navigation
Conference papers

Multiprobabilistic Venn Predictors with Logistic Regression

Abstract : This paper describes the methodology of providing multiprobability predictions for proteomic mass spectrometry data. The methodology is based on a newly developed machine learning framework called Venn machines. They allow us to output a valid probability interval. We apply this methodology to mass spectrometry data sets in order to predict the diagnosis of heart disease and early diagnoses of ovarian cancer. The experiments show that probability intervals are valid and narrow. In addition, probability intervals were compared with the output of a corresponding probability predictor.
Document type :
Conference papers
Complete list of metadatas

Cited literature [8 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-01523062
Contributor : Hal Ifip <>
Submitted on : Tuesday, May 16, 2017 - 9:16:59 AM
Last modification on : Monday, October 19, 2020 - 11:10:14 AM
Long-term archiving on: : Friday, August 18, 2017 - 12:43:37 AM

File

978-3-642-33412-2_23_Chapter.p...
Files produced by the author(s)

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

Ilia Nouretdinov, Dmitry Devetyarov, Brian Burford, Stephane Camuzeaux, Aleksandra Gentry-Maharaj, et al.. Multiprobabilistic Venn Predictors with Logistic Regression. 8th International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2012, Halkidiki, Greece. pp.224-233, ⟨10.1007/978-3-642-33412-2_23⟩. ⟨hal-01523062⟩

Share

Metrics

Record views

302

Files downloads

178