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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.
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Submitted on : Tuesday, May 16, 2017 - 9:16:59 AM
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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⟩



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