In an era of growing data complexity and volume and the advent of big data, feature
selection has a key role to play in hel** reduce high-dimensionality in machine learning …

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

In this paper, we propose a probabilistic kernel approach to preference learning based on
Gaussian processes. A new likelihood function is proposed to capture the preference …

Over the last few years, the dimensionality of datasets involved in data mining applications
has increased dramatically. In this situation, feature selection becomes indispensable as it …

In this paper, we discuss how to model preferences from a collection of ratings provided by a
panel of consumers of some kind of food product. We emphasize the role of tasting sessions …

We consider a peer-to-peer logistics system, where the agents and the platform
communicate in both directions in a multi-period setting. The goal of the platform is to select …

Preferences provide a means for specifying the desires of a decision maker (DM) in a
declarative way. In this paper, based on a DM's pairwise preferences, we infer the DM's …

The benefits of livestock breeders are usually closely related to the weight of their animals.
In this paper we present a method to anticipate the weight of each animal provided we know …

The paper is devoted to the application of Fractional Calculus concepts to modeling,
identification and control of discrete-time systems. Fractional Difference Equations (FÄE) …

Abstract Background and Objectives The traditional biomedical image retrieval methods as
well as content-based image retrieval (CBIR) methods originally designed for non …