Iterative re-weighted covariates selection for robust feature selection modelling in the presence of outliers (irCovSel)

A new method for feature selective modelling in the presence of outliers is presented. The method is a combination of iterative re-weighted partial least squares and the covariates selection approach. The method relies on iterative down-weighting of the outlying samples prior to estimating the squared covariance for covariates selection. In this way, the outlying samples carrying low weights have minimal influence on the squared covariance estimation, while the inliers have the maximum influence on the squared covariance estimation. The method allows selecting robust features, and models based on such features in general perform better in terms of prediction accuracy (lower error) than selecting features using equal sample weights for all samples. The algorithm description and tests of the method in single and multiple response scenarios are presented. Method performance is also demonstrated on a real spectral data set.

Automated summary

A new method using iterative re-weighted partial least squares and covariates selection is presented for feature selective modelling in the presence of outliers. The method iteratively down-weights the outlying samples to minimize their influence on the squared covariance estimation for selecting robust features. It is shown that models based on such features outperform those using equal sample weights in terms of prediction accuracy. The method is tested in different scenarios and its performance is demonstrated on a real spectral data set.