Paper: http://www.public.asu.edu/~huanliu/papers/icml03.pdf
Implementation of the FCBF algorithm.
Input file format:
Row: observation vector, Col: Feature/Variable vector
Usage:
In Python, call
fcbf_wrapper(inpath, thresh, delim=',', header=False, classAt=-1)
OR
From command line,
> python fcbf.py -h
usage: fcbf.py [-h] [-inpath] [-thresh] [-delim] [-header] [-classAt]
Fast Correlation-Based Filter Selection (FCBF)
optional arguments:
-h, --help show this help message and exit
-inpath Path to input file
-thresh SU threshold
-delim File delimiter
-header Contains header?
-classAt Index of class column
> python fcbf.py -inpath='../data/lungcancer.csv' -thresh=0.05
Reading file. Please wait ...
Success! Dimensions: 32 x 57
Performing FCBF selection. Please wait ...
Done!
#Features selected: 6
Selected feature indices:
[[ 0.32054501 39. ]
[ 0.32017586 19. ]
[ 0.19562365 55. ]
[ 0.15251083 1. ]
[ 0.12478091 9. ]
[ 0.07640196 2. ]]
File saved successfully. Path: ../data/features_lungcancer.csv