Soft Labeling and Expectation-Maximization Algorithm to the Learning Problem of Uncertain Labeling in Vehicular Networks

Abstract

The supervised and semi-supervised learning framework does not always correspond to the situations encountered in vehicular networks. The labeling work is therefore often laborious and expensive. This is why the development of solutions to deal with imperfect labels was of particular interest to us during this research article. We introduce in this paper the formulation of the classification problem when the information available on the labels of the examples used for learning is imperfect. We also present an efficient way to solve classification problems, even when some of the labels provided to learn the classification function are wrong. Furthermore, we present our work on the extension of statistical learning methods in the environment of vehicular networks. To build an expectation-maximization (EM) algorithm capable of optimizing the marginal log-likelihood of the observed data, the path we will take follows the classic approach encountered in the probabilistic framework. Different experiments were carried out to analyze the behavior of our algorithm when “soft” labels in vehicles are used. These experiences have allowed us in particular to highlight the contribution of "soft" labels in this context to represent information on the reliability of the labels and thus significantly improve the performance of vehicular networks.