Rough set theory is a new mathematical approach to imperfect knowledge. The problem of imperfect knowledge has been tackled for a long time by philosophers, logicians and mathematicians. Recently it became also a crucial issue for computer scientists, particularly in the area of artificial intelligence.

codeofroughset\data reduction with fuzzy rough sets or fuzzy mutual information\demo.m
..............\................................................................\entropy.m
..............\................................................................\entropy_interval.m
..............\................................................................\fs_con_N.m
..............\................................................................\fs_entropy.asv
..............\................................................................\fs_entropy.m
..............\................................................................\fs_neighbor.asv
..............\................................................................\fs_neighbor.m
..............\................................................................\kersim.m
..............\................................................................\kersim_crisp.m
..............\fuzzy preference rough set based feature evaluation and selection\FGC.m
..............\.................................................................\FLC.m
..............\.................................................................\FS_PL_FRS.m
..............\.................................................................\FS_PL_RS.m
..............\.................................................................\FUC.m
..............\.................................................................\GC.m
..............\.................................................................\LC.m
..............\.................................................................\UC.m
..............\kernelized fuzzy rough set based feature evaluation selection\certainty_s_gs.m
..............\.............................................................\certainty_theta_gs.m
..............\.............................................................\dependency_s_gs.m
..............\.............................................................\dependency_theta_gs.m
..............\.............................................................\FS_GKFS.m
..............\.............................................................\Ranking heterogeneous features with mRMR and mutual information\MI_mRMR.m
..............\KNN classifier\KNN.m
..............\neighborhood classifier\neighborhood classifier\KNN.m
..............\.......................\.......................\NEC.m
..............\.............mutual information based feature evaluation and selection\FS_FW_NE.m
..............\......................................................................\NMI.m
..............\Neighborhood rough set based feature evaluation and reduction\clsf_dpd.m
..............\.............................................................\clsf_dpd_fast.m
..............\.............................................................\clsf_dpd_fast2.m
..............\.............................................................\clsf_dpd_fast_3.m
..............\.............................................................\NRS_FW_FS.m
..............\kernelized fuzzy rough set based feature evaluation selection\Ranking heterogeneous features with mRMR and mutual information
..............\neighborhood classifier\neighborhood classifier
..............\data reduction with fuzzy rough sets or fuzzy mutual information
..............\fuzzy preference rough set based feature evaluation and selection
..............\kernelized fuzzy rough set based feature evaluation selection
..............\KNN classifier
..............\neighborhood classifier
..............\neighborhood mutual information based feature evaluation and selection
..............\Neighborhood rough set based feature evaluation and reduction
codeofroughset

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