Abstract Multi-label classification is becoming increasingly ubiquitous, but not much attention has been paid to interpretability. In this paper, we develop a multi-label classifier that can be represented as concise set of simple “if-then” rules, and thus, it offers better interpretability compared black-box models. Notably, our method able find small relevant patterns lead accurate classifica...

Fuzzy rule-based classification system (FRBCS) is a popular machine learning technique for classification purposes. One of the major issues when applying it on imbalanced data sets is its biased to the majority class, such that, it performs poorly in respect to the minority class. However many cases the minority classes are more important than the majority ones. In this paper, we have extended ...

This paper considers the automatic design of fuzzy rule-basedclassification systems based on labeled data. The classification performance andinterpretability are of major importance in these systems. In this paper, weutilize the distribution of training patterns in decision subspace of each fuzzyrule to improve its initially assigned certainty grade (i.e. rule weight). Ourapproach uses a punish...

Abstract. Constraint management plays an important role in design applications where constraints re ect design restrictions and design decisions. ECA rules are a widely used mechanism to enforce constraints. The paper argues that such rules must be augmented for design environments by a spatial and a temporal dimension of validity, resulting in so-called area-event-condition-action (AECA) rules...

this paper considers the automatic design of fuzzy rule-basedclassification systems based on labeled data. the classification performance andinterpretability are of major importance in these systems. in this paper, weutilize the distribution of training patterns in decision subspace of each fuzzyrule to improve its initially assigned certainty grade (i.e. rule weight). ourapproach uses a punish...

This paper tackles the problem of usability and security in access control mechanisms. A theoretical solution for this problem is presented using the combination of automatic rule learning and user interaction. The result is the interactive rule learning approach. Interactive rule learning is designed to complete attribute-based access control to generate concise rule sets even by non-expert en...