Learning from data using the R package "frbs"

Lala Septem Riza, Christoph Bergmeir, Francisco Herrera, Jose Manuel Benítez

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

22 Citations (Scopus)


Learning from data is a process to construct a model according to available training data so that it can be used to make predictions for new data. Nowadays, several software libraries are available to carry out this task, frbs is an R package which is aimed to construct models from data based on fuzzy rule based systems (FRBSs) by employing learning procedures from Computational Intelligence (e.g., neural networks and genetic algorithms) to tackle classification and regression problems. For the learning process, frbs considers well-known methods, such as Wang and Mendel's technique, ANFIS, Hy-FIS, DENFIS, subtractive clustering, SLAVE, and several others. Many options are available to perform conjunction, disjunction, and implication operators, defuzzification methods, and membership functions (e.g., triangle, trapezoid, Gaussian, etc). It has been developed in the R language which is an open-source analysis environment for scientific computing. In this paper, we also provide some examples on the usage of the package and a comparison with other software libraries implementing FRBSs. We conclude that frbs should be considered as an alternative software library for learning from data.

Original languageEnglish
Title of host publicationProceedings of the 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
Subtitle of host publicationJuly 6 – 11, 2014, Beijing, China
EditorsDimitar P. Filev
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages7
ISBN (Electronic)9781479920723
ISBN (Print)9781479920730
Publication statusPublished - 2014
Externally publishedYes
EventIEEE International Conference on Fuzzy Systems 2014 - Beijing International Convention Center, Beijing, China
Duration: 6 Jul 201411 Jul 2014
Conference number: 23rd
https://ewh.ieee.org/conf/wcci/2014/index.htm (Conference details)
https://ieeexplore.ieee.org/xpl/conhome/6880680/proceeding (Proceedings)


ConferenceIEEE International Conference on Fuzzy Systems 2014
Abbreviated titleFUZZ-IEEE 2014
Internet address

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