粗糙集的基本算法我要分享

Basic algorithm of rough set

粗糙集约简 粗糙集-约简 fuzzy-rough 约简 codeofroughset

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代码分类: 一般算法

开发平台: matlab

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代码描述

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.


代码预览

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