In this study, we demonstrate two tasks: regression and classification. For regression, we consider two data sets which have already included in the frbs package: Gas Furnace and Mackey Glass. Besides using regression methods included in the frbs package, some other packages are applied for comparison, such as randomForrest, RSNNS, fRegression, nnet, CORElearn, and e1071. In classification, three data sets, which are Iris, Pima, and Wine, are considered. These datasets are splitted based on the 5-fold validation. Other packages are used in this experiments: CORElearn, C50, randomForrest, nnet, RSNNS, tree, kernlab, and fugeR. The following is a list of materials for the experiments:

- Regression:
- Gas Furnace Data Set: ANFIS.R; DENFIS.R; FIR.DM.R; fRegression.R; FS.HGD.R; GFS.FR.MOGUL.R; HyFIS.R; locallyWR.R; nnet.R; randomForest.R; RSNNS.R; SBC.R; SVM.R; Thrift.R; WM.R.
- Mackey Glass Data Set: ANFIS.R; DENFIS.R; FIR.DM.R; fRegression.R; FS.HGD.R; GFS.FR.MOGUL.R; HyFIS.R; locallyWR.R; nnet.R; randomForest.R; RSNNS.R; SBC.R; SVM.R; Thrift.R; WM.R.

- Classification:
- Iris Data Set: C50.R; FH.GBML.R; FRBCS.CHI.R; FRBCS.W.R; fugeR.R; GFS.GCCL.R; knn.R; randomForest.R; RSNNS.R; SBC.R; SLAVE.R; SVM.R; tree.R.
- Pima Data Set: C50.R; FH.GBML.R; FRBCS.CHI.R; FRBCS.W.R; fugeR.R; GFS.GCCL.R; knn.R; randomForest.R; RSNNS.R; SBC.R; SLAVE.R; SVM.R; tree.R.
- Wine Data Set: C50.R; FH.GBML.R; FRBCS.CHI.R; FRBCS.W.R; fugeR.R; GFS.GCCL.R; knn.R; randomForest.R; RSNNS.R; SBC.R; SLAVE.R; SVM.R; tree.R.