Une comparaison des algorithmes d'apprentissage pour la survie avec données manquantes

Survival analysis is an essential tool for the study of health data. An inherent component of such data is the presence of missing values. In recent years, researchers proposed new learning algorithms for survival tasks based on neural networks. Here, we studied the predictive performance of such algorithms coupled with different methods for handling missing values on simulated data that reflect a realistic situation, i.e., when individuals belong to unobserved clusters. We investigated different patterns of missing data. The results show that, without further feature engineering, no single imputation method is better than the others in all cases. The proposed methodology can be used to compare other missing data patterns and/or survival models. The Python code is accessible via the package survivalsim. -- L'analyse de survie est un outil essentiel pour l'\'etude des donn\'ees de sant\'e. Une composante inh\'erente \`a ces donn\'ees est la pr\'esence de valeurs manquantes. Ces derni\`eres ann\'ees, de nouveaux algorithmes d'apprentissage pour la survie, bas\'es sur les r\'eseaux de neurones, ont \'et\'e con\c{c}us. L'objectif de ce travail est d'\'etudier la performance en pr\'ediction de ces algorithmes coupl\'es \`a diff\'erentes m\'ethodes pour g\'erer les valeurs manquantes, sur des donn\'ees simul\'ees qui refl\`etent une situation rencontr\'ee en pratique, c'est-\`a dire lorsque les individus peuvent \^etre group\'es selon leurs covariables. Diff\'erents sch\'emas de donn\'ees manquantes sont \'etudi\'es. Les r\'esultats montrent que, sans l'ajout de variables suppl\'ementaires, aucune m\'ethode d'imputation n'est meilleure que les autres dans tous les cas. La m\'ethodologie propos\'ee peut \^etre utilis\'ee pour comparer d'autres mod\`eles de survie. Le code en Python est accessible via le package survivalsim.

Finding optimal Pulse Repetion Intervals with Many-objective Evolutionary Algorithms

In this paper we consider the problem of finding Pulse Repetition Intervals allowing the best compromises mitigating range and Doppler ambiguities in a Pulsed-Doppler radar system. We revisit a problem that was proposed to the Evolutionary Computation community as a real-world case to test Many-objective Optimization algorithms. We use it as a baseline to compare several Evolutionary Algorithms for black-box optimization with different metrics. Resulting data is aggregated to build a reference set of Pareto optimal points and is the starting point for further analysis and operational use by the radar designer.