jranaraki/thresholdfuzzyroughquickreduct

This is an implementation of improved Fuzzy Rough QuickReduct algorithm

title	author	date
ThresholdFuzzyRoughQuickReduct	Javad Rahimipour Anaraki	23/05/18

Use case

To determine the most important features using an improved version of Fuzzy-Rough QuickReduct algorithm as described in Improving fuzzy-rough quick reduct for feature selection By Javad Rahimipour Anaraki and Mahdi Eftekhari

More info can be found in Novel Improvements on the Fuzzy-Rough QuickReduct Algorithm

Compile

This code can be run using MATLAB R2006a and above

Run

To run the code, open main.m and choose a dataset to apply the method to. At first, the overall dependency degree is calculated using dependency.m. Then, indisernible objects are determined and returned to main.m. Finally, feature are selected if they have the best dependecy degree among all. This process stops if (overall dependency degree - current dependency degree) x number of samples < 1 or maximum dependency degree does not improve with adding extra features.

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

Note

Datasets should have no column and/or row names, and the class values should be all numeric