中文说明：粗糙集的基本算法，包括数据补齐，属性约简，值约简，规则生成，非常实用。
English Description：
The basic algorithm of rough set, including data completion, attribute reduction, value reduction, rule generation, is very practical.
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中文说明：粗糙集的基本算法，包括数据补齐，属性约简，值约简，规则生成，非常实用。
English Description：
The basic algorithm of rough set, including data completion, attribute reduction, value reduction, rule generation, is very practical.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
..............\neighborhood 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