jranaraki / FuzzyRoughQuickReduct

This is an implementation of Fuzzy Rough QuickReduct algorithm

title	author	date
FuzzyRoughQuickReduct	Javad Rahimipour Anaraki	08/02/18

Use case

To determine the most important features using the algorithm described in New Approaches to Fuzzy-Rough Feature Selection by Richard Jensen and Qiang Shen

Compile

To compile the C++ code follow these steps:

1. Be sure that you have the latest GCC/G++ compiler installed

2. Use g++ -o FRQR FRQR.cpp -std=c++11 to compile the program

3. To improve its performance one can use -O1 or -O2 or -O3

4. Ignore the following warning message:

    FRQR.cpp:238:14: warning: expression result unused [-Wunused-value]
    for (s;s<cls[nCls];++s) {
    ^
    1 warning generated.
   

For the MATLAB code, simply copy FRQR.m and IND.m to a folder containing a sub-folder called Data. Place your dataset in that folder and add the name of the dataset to FRQR.m file and run the code.

Run

To run the program use ./FRQR {a dataset name}

Note

The classification outcome column of dataset should be sorted ascendingly