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sCWC

sCWC: very fast feature selection for nominal data

Quick Start

Install sbt. Clone or download this repository.

$ git clone https://github.com/tkub/scwc.git

To build scwc.jar file in the ./bin directory, run

$ cd scwc
$ sbt assembly

Command bin/scwc accepts a number of command line options.

$ bin/scwc --help
scwc 0.8.2
Usage: scwc [options] inputfile [outputfile]

  --help                  prints this usage text
  inputfile               input file in the ARFF/CSV/LIBSVM format
  outputfile              output file with extension {arff, csv, libsvm}
  -s, --sort {mi|su|icr|mcc}  
                          sorting measure for forward selection (default: mi)
  -v, --verbose           display selection process information
  -l, --log               output log file
  -o, --overwrite         overwrite output file
  -r, --remove <range>    attribute indices to remove e.g.) 1,3-5,8

It requires an input file in the dense/sparse ARFF, CSV, or LIBSVM formats. Unless an output file is specified, it creates a new data file in the same format as the input file after removing the unselected features from the input file.

$ bin/scwc -v data/sparse.arff

In the above example, data/sparse.out.arff is created. When an output file is specified, the output format is given by its file extension (now arff, csv, and libsvm are available).

$ bin/scwc -s su -l data/dense.arff data/dense.csv

Also the log file data/dense.out.log is created by the option -l. If you want to remove the 1st column in the input file, run with the option -r 1.

$ scwc -v -l data/sample.csv -r 1

Format of input files

You can use not only binary values {0,1} but also any nominal values, for example, {a,b,c,d} available in the ARFF format.

If you want to use ordinal variables, use the dummy variables to encode the values. For example, values 1,2, and 3 can be encoded as [0,0], [1,0] and [1,1] respectively. This encoding procedure will be incorporated in the future version.

Reference

  • K. Shin, T. Kuboyama, T. Hashimoto, D. Shepard: Super-CWC and super-LCC: Super fast feature selection algorithms. Big Data 2015: 61-67

Acknowledgements

This work was partially supported by JSPS KAKENHI Grant No.26280090

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