title	author	date
FuzzyRoughShuffledFrog	Javad Rahimipour Anaraki	08/08/18

Use case

To determine the most important features using binary Fuzzy-Rough Shuffled Frog Leaping Algorithm as described in A Fuzzy-Rough Feature Selection based on Binary Shuffled Frog Leaping Algorithm By Javad Rahimipour Anaraki, Saeed Samet, Mahdi Eftekhari and Chang Wook Ahn

A long version of the paper can be accessed in A Fuzzy-Rough based Binary Shuffled Frog Leaping Algorithm for Feature Selection

Compile

To compile this program follow these steps:

1. Be sure that you have the latest GCC/G++ compiler and Java installed
2. Use g++ -o SFLA main.cpp clsWeka.cpp clsData.cpp -std=c++11 -Iinclude -pthread to compile the program
3. To improve its performance one can use -O1 or -O2 or -O3

Run

To run the program use ./SFLA /Data/{a dataset name}OK.csv {maximum number of stalled} {number of threads} {maximum iteration} For instance, run the code for Breast Tissue dataset with maximum three stall situations, four threads for 10 times, it should be run using: ./SFLA /Data/BreastTissueOK.csv 3 4 10 . The classification results will be stored in classification folder and the convergence process is stored in results folder.

All datasets are stored in data folder and originally adopted from UCI Machine Learning Repository.

Note

Datasets end with OK.csv in their names should have no column and/or row names, and the class values should be numeric and sorted in ascending order. The ones ending with W.csv should have columns name and the class values should be alphabetical.