HANDLING IMPRECISE LABELS IN FEATURE SELECTION WITH GRAPH LAPLACIAN

摘要

Feature selection is a preprocessing step of great importance for a lot of pattern recognition and machine learning applications, including classification. Even if feature selection has been extensively studied for classical problems, very little work has been done to take into account a possible imprecision or uncertainty in the assignment of the class labels. However, such a situation can be encountered frequently in practice, especially when the labels are given by a human expert having some doubts on the exact class value. In this paper, the problem where each possible class for a given sample is associated with a probability is considered. A feature selection criterion based on the theory of graph Laplacian is proposed and its interest is experimentally demonstrated when compared with basic approaches to handle such imprecise labels.