In this work, we present a Fuzzy Constraint Networks model for discrete domains, which generalizes the Disjunctive Fuzzy Temporal Constraint Networks model for temporal reasoning, and we propose the use of the seriesparallel and tree-decomposition approaches for simplifying its processing. After a separate empirical evaluation process of both techniques, a combined evaluation process over the s...

This paper presents an evolutionary learning process for linguistic modeling with weighted double-consequent fuzzy rules. These kinds of fuzzy rules are used to improve the linguistic modeling, with the aim of introducing a trade-off between interpretability and precision. The use of weighted double-consequent fuzzy rules makes more complex the modeling and learning process, increasing the solu...

In multi-label classification, where the evaluation of predictions is less straightforward than in single-label various meaningful, though different, loss functions have been proposed. Ideally, learning algorithm should be customizable towards a specific choice performance measure. Modern implementations boosting, most prominently gradient boosted decision trees, appear to appealing from this p...

This paper studies Lebesgue integral of a fuzzy closed set-valued stochastic process with respect to the time t. Firstly, a progressively measurable fuzzy closed set-valued stochastic process is discussed and an almost everywhere problem in the former Aumann type Lebesgue integral of the level-set process is pointed out. Secondly, a new definition of the Lebesgue integral by decomposable closur...