FSS - feature subset selection and feature ranking 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. [idx,score] = fss(D,cl) [idx,score] = fss(D,cl,MODE) [idx,score] = fss(D,cl,MODE) D data - each column represents a feature cl classlabel Mode 'Pearson' [default] correlation 'rank' correlation 'FSDD' feature selection algorithm based on a distance discriminant [2] %%% 'MRMR','MID','MIQ' max-relevance, min redundancy [1] - not supported yet. score score of the feature idx ranking of the feature [tmp,idx]=sort(-score) see also: TRAIN_SC, XVAL, ROW_COL_DELETION REFERENCES: [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
Package: nan