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.关注次数: 359
下载次数: 2
文件大小: 28K
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