About: Code for Calibrated AdaMEC for binary cost-sensitive classification. The method is just AdaBoost that properly calibrates its probability estimates and uses a cost-sensitive (i.e. risk-minimizing) decision threshold to classify new data.

Updated license information

About: FEAST provides implementations of common mutual information based filter feature selection algorithms (mim, mifs, mrmr, cmim, icap, jmi, disr, fcbf, etc), and an implementation of RELIEF. Written for C/C++ & Matlab.

Major refactoring of FEAST to improve speed and portability.

• FEAST now clones the input data if it's floating point and discretises it to unsigned ints once in a single pass. This improves the speed by about 30%.
• FEAST now has unsigned int entry points which avoid this discretisation and are much faster if the data is already categorical.
• Added weighted feature selection algorithms to FEAST which can be used for cost-sensitive feature selection.
• Added a Java API using JNI.
• FEAST now returns the internal score for each feature according to the criterion. Available in all three APIs.
• Rearranged the repository to make it easier to work with. Header files are now in `include`, source in `src`, the MATLAB API is in `matlab/` and the Java API is in `java/`.
• FEAST now compiles cleanly using `-std=c89 -Wall -Werror`.