Software

we would like to introduce YALE (Yet Another Learning Environment) to you:

http://yale.cs.uni-dortmund.de/

YALE is a flexible and modular environment for machine learning experiments and applications. The experimental setup can be easily described in a configuration file in a simple XML format and may consist of arbitrarily nested machine learning and data pre-processing operators and operator chains. Among others, YALE offers operators for:

* machine learning algorithms for classification, regression, and clustering including wrappers for a number of external machine learning programs like support vector machines (SVM^light, mySVM), the decision tree inducer C4.5, and all learners and clusterers offered by WEKA;

* feature selection and generation: forward selection, backward elimination, and several genetic algorithms;

* performance evaluation: cross-validation and other evaluation schemes, several performance criteria for classification and regression, operators for parameter optimization in enclosed operators or operator chains, and operators for logging and presenting results;

* in- and output: flexible operators for data in- and output, support of flexible experimental (re)arrangements, usage of (optional) meta information on data.

YALE provides an easy to use extension mechanism that makes it possible to integrate new operators and adapt YALE to your personal requirements. Since YALE is entirely written in Java, it runs on any major platform/operating system. YALE is a freely available open source software under the terms of the GNU General Public Licencse. You are weclome to use it!

Best regards, Simon Fischer, Ingo Mierswa, Oliver Ritthoff, and Ralf Klinkenberg