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 …

There is growing evidence that integrating classification and association rule mining can
produce more efficient and accurate classifiers than traditional techniques. This thesis …

Decision trees are commonly used for learning and extracting classification rules from data.
The fuzzy rule based decision tree (FRDT) is very representative owing to its better …

Multi-objective evolutionary algorithms represent an effective tool to improve the accuracy-
interpretability trade-off of fuzzy rule-based classification systems. To this aim, a tuning …

The discovery of generalized fuzzy association rules is a very important data-mining task,
because more general and qualitative knowledge can be uncovered for decision making. In …

Neuro-fuzzy systems show very good performance and the knowledge comprised within
their structure is easily interpretable. To further improve their accuracy they can be combined …

Associative classification is a branch in data mining that employs association rule discovery
methods in classification problems. In this paper, we introduce a novel data mining method …

In this paper a new method for definition of the antecedent/premise part of the fuzzy rule-
based (FRB) systems is proposed. It removes the need to define the membership functions …

Quite often, complex systems or phenomena are observed from various points of view
yielding the particular subsets of data usually being composed of locally available attributes …

Associative classification (AC) is a data mining approach that uses association rule
discovery methods to build classification systems (classifiers). Several research studies …