Feature Selection Toolbox (FST) is software primarily for feature selection in the machine learning domain,[1] written in C++, developed at the Institute of Information Theory and Automation (UTIA), of the Czech Academy of Sciences.

The first generation of Feature Selection Toolbox (FST1) was a Windows application with user interface allowing users to apply several sub-optimal, optimal and mixture-based feature selection methods on data stored in a trivial proprietary textual flat file format.

[2] The third generation of Feature Selection Toolbox (FST3) was a library without user interface, written to be more efficient and versatile than the original FST1.

FST3 is more narrowly specialized than popular software like the Waikato Environment for Knowledge Analysis Weka, RapidMiner or PRTools.

[4] By default, techniques implemented in the toolbox are predicated on the assumption that the data is available as a single flat file in a simple proprietary format or in Weka format ARFF, where each data point is described by a fixed number of numeric attributes.