Install the QuickRBF Package

After downloading the QuickRBF package, you need to build it in your system. On Unix systems, you only need to untar the source code package, then type `make' to build all the programs of package. On other systems, consult `Makefile' to build them. Also, we offer the pre-built windows binaries.

These programs are included in QuickRBF package.

Data Sets Preparation

The format of training and testing data file is:

[label] is the target value of the training data. For classification, it should be a positive integer which identifies a class (multi-class classification is supported). [index] is an integer starting from 1, and [value] is a real number. The labels in the testing data file are only used to calculate accuracy or error.

Here is an example data file.

We offer a simple tool, datatrans, in QuickRBF package to help you to convert the data file to the our format. You can use "Microsoft Excel" or other software to edit your data file, then save the data file to *.csv file, which use the "," to separate the feature values. In addition, the last value is the class feature value.

Here is an example Excel .csv data file.

The data format of Excel csv is almost the same with the UCI format, but it use the space as separator. Our datatrans program supports both formats of them.

The usage of datatrans program is

Then, you will get the formatted file naming [your data file].out.

Scaling the data

Scaling your data properly is very important in data classification experiments. So we also offer datascale program in our package to help you to scale your data. The default range is from -1 to 1, and you may edit the source file to change the setting.

The usage of datascale program is

Then, you will get the scaled file naming [data file].scale.

Here is an example data after scaling.

Selecting the Centers

How to select good centers for RBFN is still a hot research issue. In this package, we only offer a simple tool, centerselect, to select the centers from training instances randomly. We will try to find a better approach in the future. However, if your training data is not large (less than 10,000), you may consider to use the all training instances as centers.

The usage of centerselect program is

Then, you will get the center data file naming [training data file].[number of centers].

Do the Experiments

After preparing the dat file, you can use quickrbf to start your experiment.

The usage of quickrbf program is

Here is an example of satimage in Statlog data sets.