Learning from data using the R package "FRBS"
Lala Septem Riza, Christoph Bergmeir, Francisco Herrera, José M. Benítez · 2014
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.