Catherine Krier

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This paper uses Mutual Information as an alternative variable selection method for quantitative structure-property relationships data. To evaluate the performance of this criterion, the enantioselectivity of 67 molecules, in three different chiral stationary phases, is modelled. Partial Least Squares together with three commonly used variable selection(More)
Hypoxic events are common in newborns but their consequences on brain development have not been demonstrated. It has been reported that in newborn animal models of cerebral hypoxic-ischaemic insult, short-term hypoxia before the insult completely prevented brain damage. The mechanisms of this brain tolerance have not been fully elucidated. Using a rat model(More)
Spectral data often have a large number of highly-correlated features, making feature selection both necessary and uneasy. A methodology combining hierarchical constrained clustering of spectral variables and selection of clusters by mutual information is proposed. The clustering allows reducing the number of features to be selected by grouping similar and(More)
Prediction problems from spectra are largely encountered in chemometry. In addition to accurate predictions, it is often needed to extract information about which wavelengths in the spectra contribute in an effective way to the quality of the prediction. This implies to select wavelengths (or wavelength intervals), a problem associated to variable(More)
Spectrometric data involve very high-dimensional observations representing sampled spectra. The correlation of the resulting spectral variables and their high number are two sources of difficulties in modeling. This paper proposes a supervised feature clustering algorithm that provides dimension reduction for this type of data in a classification context.(More)
Plants are subjected to continuous stimuli from the environment and have evolved an ability to respond through various growth and development processes. Phototropism and gravitropism responses enable the plant to reorient with regard to light and gravity. We quantified the speed of maritime pine seedlings to reorient with regard to light and gravity over 22(More)
Selecting relevant features in mass spectra analysis is important both for classification and search for causality. In this paper, it is shown how using mutual information can help answering to both objectives, in a model-free nonlinear way. A combination of ranking and forward selection makes it possible to select several feature groups that may lead to(More)
Computer technologies have revolutionised the processing of information and the search for knowledge. With the ever increasing computational power, it is becoming possible to tackle new data analysis applications as diverse as mining the Internet resources, analysing drugs effects on the organism or assisting wardens with autonomous video detection(More)
Although neural networks are commonly encountered to solve classification problems, ranking data present specificities which require adapting the model. Based on a latent utility function defined on the characteristics of the objects to be ranked, the approach suggested in this paper leads to a perceptron-based algorithm for a highly non linear model. Data(More)
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