The year 2015 marks the 30th birthday of DC (Difference of Convex functions) programming
and DCA (DC Algorithms) which constitute the backbone of nonconvex programming and …

This paper offers a comprehensive approach to feature selection in the scope of
classification problems, explaining the foundations, real application problems and the …

Feature selection (FS) is an important component of many pattern recognition tasks. In these
tasks, one is often confronted with very high-dimensional data. FS algorithms are designed …

Feature selection problems have been extensively studied in the setting of linear estimation
(eg LASSO), but less emphasis has been placed on feature selection for non-linear …

Feature selection is one of the most important dimension reduction techniques for its
efficiency and interpretation. Since practical data in large scale are usually collected without …

Ranking problems, also known as preference learning problems, define a widely spread
class of statistical learning problems with many applications, including fraud detection …

Learning-to-rank is one of the learning frameworks in machine learning and it aims to
organize the objects in a particular order according to their preference, relevance or ranking …

Learning to rank is an increasingly important scientific field that comprises the use of
machine learning for the ranking task. New learning to rank methods are generally …

Fetal heart rate (FHR) monitoring is routinely used in clinical practice to help obstetricians
assess fetal health status during delivery. However, early detection of fetal acidosis that …

High dimensionality is quite commonly encountered in data mining problems, and hence
dimensionality reduction becomes an important task in order to improve the efficiency of …