A novel neuro-fuzzy model named fuzzy broad learning system (BLS) is proposed by
merging the Takagi-Sugeno (TS) fuzzy system into BLS. The fuzzy BLS replaces the feature …

Fuzzy rule-based systems (FRBSs) is a rule-based system which uses linguistic fuzzy
variables as antecedents and consequent to represent human-understandable knowledge …

Nowadays, a huge amount of data are generated, often in very short time intervals and in
various formats, by a number of different heterogeneous sources such as social networks …

The fuzzy broad learning system (FBLS) is a recently proposed neuro-fuzzy model that
shares the similar structure of a broad learning system (BLS). It shows high accuracy in both …

As big data often contains a significant amount of uncertain, unstructured, and imprecise
data that are structurally complex and incomplete, traditional attribute reduction methods are …

Learning an interval-valued fuzzy rule-based classification system is a challenge as its
success directly depends on the interval-valued fuzzy partition used. In fact, the learning of …

The adoption of IoT for smart health applications is a relevant tool for distributed and
intelligent automatic diagnostic systems. This work proposes the development of an …

Graph-based clustering aims to partition the data according to a similarity graph, which has
shown impressive performance on various kinds of tasks. The quality of similarity graph …

Interpretability has always been a major concern for fuzzy rule-based classifiers. The usage
of human-readable models allows them to explain the reasoning behind their predictions …

Data stream mining has recently grown in popularity, thanks to an increasing number of
applications which need continuous and fast analysis of streaming data. Such data are …