Different algorithms for feature selection to help navigate in vast amount of data
The Algorithms are based upon the following research articles:
VIP: A review of variable selection methods in Partial Least Squares Regression. Tahir Mehmood , Kristian Hovde Liland, Lars Snipen, Solve Sæbø Biostatistics, Department of Chemistry, Biotechnology and Food Sciences, Norwegian University of Life Sciences, Norway DOI: https://doi.org/10.1016/j.chemolab.2012.07.010
sMC: T.N. Tran*, N.L. Afanador, L.M.C. Buydens, L. Blanchet, Interpretation of variable importance in Partial Least Squares with Significance Multivariate Correlation (sMC), Chemometrics and Intelligent Laboratory Systems, Volume 138, 15 November 2014, Pages 153-160 DOI: http://dx.doi.org/10.1016/j.chemolab.2014.08.005
IPW: M. Forina, C. Casolino, C. Pizarro Millan, Iterative predictor weighting (IPW) PLS: a technique for the elimination of useless predictors in regression problems, Journal of Chemometrics 13 (1999) 165-184. DOI: 10.1002/(SICI)1099-128X(199903/04)13:23.3.CO;2-P