Abstract
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 wellknown methods, such as Wang and Mendel's technique, ANFIS, HyFIS, 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 opensource 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 language  English 

Title of host publication  Proceedings of the 2014 IEEE International Conference on Fuzzy Systems (FUZZIEEE) 
Subtitle of host publication  July 6 – 11, 2014, Beijing, China 
Editors  Dimitar P. Filev 
Place of Publication  Piscataway NJ USA 
Publisher  IEEE, Institute of Electrical and Electronics Engineers 
Pages  21492155 
Number of pages  7 
ISBN (Electronic)  9781479920723 
ISBN (Print)  9781479920730 
DOIs  
Publication status  Published  2014 
Externally published  Yes 
Event  IEEE International Conference on Fuzzy Systems 2014  Beijing International Convention Center, Beijing, China Duration: 6 Jul 2014 → 11 Jul 2014 https://ewh.ieee.org/conf/wcci/2014/index.htm (Conference details) 
Conference
Conference  IEEE International Conference on Fuzzy Systems 2014 

Abbreviated title  FUZZIEEE 2014 
Country  China 
City  Beijing 
Period  6/07/14 → 11/07/14 
Internet address 

Cite this
}
Learning from data using the R package "frbs". / Riza, Lala Septem; Bergmeir, Christoph; Herrera, Francisco; Benítez, Jose Manuel.
Proceedings of the 2014 IEEE International Conference on Fuzzy Systems (FUZZIEEE): July 6 – 11, 2014, Beijing, China. ed. / Dimitar P. Filev. Piscataway NJ USA : IEEE, Institute of Electrical and Electronics Engineers, 2014. p. 21492155 6891650.Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peerreview
TY  GEN
T1  Learning from data using the R package "frbs"
AU  Riza, Lala Septem
AU  Bergmeir, Christoph
AU  Herrera, Francisco
AU  Benítez, Jose Manuel
PY  2014
Y1  2014
N2  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 wellknown methods, such as Wang and Mendel's technique, ANFIS, HyFIS, 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 opensource 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.
AB  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 wellknown methods, such as Wang and Mendel's technique, ANFIS, HyFIS, 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 opensource 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.
UR  http://www.scopus.com/inward/record.url?scp=84912571693&partnerID=8YFLogxK
U2  10.1109/FUZZIEEE.2014.6891650
DO  10.1109/FUZZIEEE.2014.6891650
M3  Conference Paper
SN  9781479920730
SP  2149
EP  2155
BT  Proceedings of the 2014 IEEE International Conference on Fuzzy Systems (FUZZIEEE)
A2  P. Filev, Dimitar
PB  IEEE, Institute of Electrical and Electronics Engineers
CY  Piscataway NJ USA
ER 