5 research outputs found

    Multiprobabilistic Venn Predictors with Logistic Regression

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    Part 4: First Conformal Prediction and Its Applications Workshop (COPA 2012)International audienceThis 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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