Abstract System modeling with fuzzy rule-based systems (FRBSs), ie fuzzy modeling (FM),
usually comes with two contradictory requirements in the obtained model: the interpretability …

Fuzzy inference systems (FIS) are widely used for process simulation or control. They can be
designed either from expert knowledge or from data. For complex systems, FIS based on …

Although fuzzy systems demonstrated their ability to solve different kinds of problems in
various applications, there is an increasing interest on augmenting them with learning …

Data-mining is the process of extracting desirable knowledge or interesting patterns from
existing databases for specific purposes. Most conventional data-mining algorithms identify …

The data-driven approach is popular to automate learning of fuzzy rules and tuning
membership function parameters in fuzzy inference systems (FIS) development. However …

Tuning fuzzy rule-based systems for linguistic fuzzy modeling is an interesting and widely
developed task. It involves adjusting some of the components of the knowledge base without …

Due to the increasing use of very large databases and data warehouses, mining useful
information and helpful knowledge from transactions is evolving into an important research …

A portfolio is a combination of a number of securities. Portfolio analysis is a quantitative
method for selecting an optimal portfolio that can strike a balance between maximizing the …

Data mining is the process of extracting desirable knowledge or interesting patterns from
existing databases for specific purposes. Most conventional data-mining algorithms identify …

This work proposes the application of Multi-Objective Genetic Algorithms to obtain Fuzzy
Rule-Based Systems with a better trade-off between interpretability and accuracy in …