The Variable Precision Rough Set Inductive Logic Programming model (VPRSILP model) extends the Variable Precision Rough Set (VPRS) model to Inductive Logic Programming (ILP). The VPRSILP model is considered from the Statistical Relational Learning perspective, by comparing and contrasting it with Stochastic Logic Programs.

The Variable Precision Rough Set Inductive Logic Programming model (VPRSILP model) extends the Variable Precision Rough Set (VPRS) model to Inductive Logic Programming (ILP). This paper presents cVPRSILP, an approach based on the VPRSILP model, that uses attributes based on clauses of interest to define the elementary sets. An illustrative experiment using the Predictive Toxicology Evaluation C...

Sharing economy applications need to coordinate humans, each of whom may have different preferences over the provided service. Traditional approaches model this as a resource allocation problem and solve it by identifying matches between users and resources. These require knowledge of user preferences and, crucially, assume that they act deterministically or, equivalently, that each of them is ...

Load forecasting in buildings and homes has been in the last years a task of increasing importance. New services and functionalities can be offered in the home environment due to this predictions, for instance, the detection of potential demand response programs and peaks that may increase the energy bill in a dynamic tariff framework. Almost real-time predictions are key for these services but...