nan_confusionmat Classification nan_kappa

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nan_fss

feature subset selection and feature ranking

Calling Sequence

[idx,score] = nan_fss(D,cl)
[idx,score] = nan_fss(D,cl,MODE)
[idx,score] = nan_fss(D,cl,MODE)

Parameters

D :

data - each column represents a feature

cl:

classlabel

Mode = 'Pearson':

[default] correlation

Mode = 'rank':

correlation

Mode ='FSDD':

feature selection algorithm based on a distance discriminant [2]

Mode = 'MRMR','MID','MIQ':

max-relevance, min redundancy [1] - not supported yet.

score :

score of the feature

idx:

ranking of the feature

Description

the method is motivated by the max-relevance-min-redundancy (mRMR) approach [1]. However, the default method uses partial correlation, which has been developed from scratch. PCCM [3] describes a similar idea, but is more complicated. An alternative method based on FSDD is implemented, too.

See also

Bibliography

[1] Peng, H.C., Long, F., and Ding, C.,

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.

[2] Jianning Liang, Su Yang, Adam Winstanley,

Invariant optimal feature selection: A distance discriminant and feature ranking based solution,

Pattern Recognition, Volume 41, Issue 5, May 2008, Pages 1429-1439.

ISSN 0031-3203, DOI: 10.1016/j.patcog.2007.10.018.

[3] K. Raghuraj Rao and S. Lakshminarayanan

Partial correlation based variable selection approach for multivariate data classification methods

Chemometrics and Intelligent Laboratory Systems

Volume 86, Issue 1, 15 March 2007, Pages 68-81

http://dx.doi.org/10.1016/j.chemolab.2006.08.007

Authors

nan_confusionmat Classification nan_kappa