User:Pasiluukka/sandbox

Fuzzy entropy and similarity based feature selection method (FES) was created in 2011. It is a feature selection method for selecting most important features in any classification problems. Basic idea in short is to use training data to create ideal vectors for each class and then compute similarities of the samples and ideal vectors. Once this has been done next step is to use similarity values to calculate fuzzy entropy values for each feature. Lower entropy values indicate structureness while higher entropy values indicate randomness and hence lower importance. Algorithm in short goes as follows.

FES has been implemented in matlab, python and R.