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常見的一種特征篩選手段,可以從大量變量中篩選特征變量實現保留變量與目標之間的最大相關性,而彼此間的重復信息最小

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代碼片段和文件信息

function?[fea]?=?mrmr_mid_d(d?f?K)
%?function?[fea]?=?mrmr_mid_d(d?f?K)
%
%?The?MID?scheme?of?minimum?redundancy?maximal?relevance?(mRMR)?feature?selection
%?
%?The?parameters:
%??d?-?a?N*M?matrix?indicating?N?samples?each?having?M?dimensions.?Must?be?integers.
%??f?-?a?N*1?matrix?(vector)?indicating?the?class/category?of?the?N?samples.?Must?be?categorical.
%??K?-?the?number?of?features?need?to?be?selected
%
%?Note:?This?version?only?supports?discretized?data?thus?if?you?have?continuous?data?in?“d“?you?
%???????will?need?to?discretize?them?first.?This?function?needs?the?mutual?information?computation?
%???????toolbox?written?by?the?same?author?downloadable?at?the?Matlab?source?codes?exchange?site.?
%???????Also?There?are?multiple?newer?versions?on?the?Hanchuan?Peng‘s?web?site?
%???????(http://research.janelia.org/peng/proj/mRMR/index.htm).
%
%?More?information?can?be?found?in?the?following?papers.
%
%?H.?Peng?F.?Long?and?C.?Ding?
%???“Feature?selection?based?on?mutual?information:?criteria?
%????of?max-dependency?max-relevance?and?min-redundancy“
%???IEEE?Transactions?on?Pattern?Analysis?and?Machine?Intelligence
%???Vol.?27?No.?8?pp.1226-1238?2005.?
%
%?C.?Ding?and?H.?Peng?
%???“Minimum?redundancy?feature?selection?from?microarray?gene?
%????expression?data“?
%????Journal?of?Bioinformatics?and?Computational?Biology
%???Vol.?3?No.?2?pp.185-205?2005.?
%
%?C.?Ding?and

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