GILET , Cyprien

Machine Learning algorithms become more and more promising in precision medicine. From the clinical or biological profile of each patient, these methods can for example help the experts in the application field to diagnose a disease or to predict a response to a specific treatment. To this aim, supervised learning classifiers are fitted from a...

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Workshop on Artificial Intelligence organized by University Côte d'Azur

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In this paper, we present the optimization procedure for computing the discrete boxconstrained minimax classifier introduced in [1, 2]. Our approach processes discrete or beforehand discretized features. A box-constrained region defines some bounds for each class proportion independently. The box-constrained minimax classifier is obtained from...

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This paper aims to build a supervised classifier for dealing with imbalanced datasets, uncertain class proportions, dependencies between features, the presence of both numeric and categorical features, and arbitrary loss functions. The Bayes classifier suffers when prior probability shifts occur between the training and testing sets. A solution...

This paper deals with unsupervised clustering with feature selection. The problem is to estimate both labels and a sparse projection matrix of weights. To address this combina-torial non-convex problem maintaining a strict control on the sparsity of the matrix of weights, we propose an alternating minimization of the Frobenius norm criterion....

This paper proposes a new approach for dealing with imbalanced classes and prior probability shifts in supervised classification tasks. Coupled with any feature space partitioning method, our criterion aims to compute an almost-Bayesian randomized equalizer classifier for which the maxima of the class-conditional risks are minimized. Our...

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Ce papier propose une nouvelle approche ajustant les réseaux de neurones convolutifs appliqués sur des jeux de données déséquilibrés dont les proportions par classes sont incertaines. La règle de décision constitutant la sortie du réseau de neurones est remplacée par le classifieur Minimax dont la particularité est de chercher à égaliser les...