Fuzzy associative classification has not been widely analyzed in the literature, although
associative classifiers (ACs) have proved to be very effective in different real domain …

In this paper, we propose an efficient distributed fuzzy associative classification model
based on the MapReduce paradigm. The learning algorithm first mines a set of fuzzy …

Associative classifiers have proven to be very effective in classification problems.
Unfortunately, the algorithms used for learning these classifiers are not able to adequately …

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 …

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 …

Fuzzy decision trees (FDTs) have shown to be an effective solution in the framework of fuzzy
classification. The approaches proposed so far to FDT learning, however, have generally …

Abstract The previous Fuzzy Rule-Based Classification Systems (FRBCSs) for Big Data
problems consist in concurrently learning multiple Chi et al. FRBCSs whose rule bases are …

Classification is a technique widely and successfully used for prediction, which is one of the
most attractive features of data mining. However, building the classifier is the most …

Nowadays, the call for transparency in Artificial Intelligence models is growing due to the
need to understand how decisions derived from the methods are made when they ultimately …

Associative classification (AC) is a research topic that integrates association rules with
classification in data mining to build classifiers. After dissemination of the Classification …