Feature selection can be performed by the Least Absolute Shrinkage and Selection Operator (LASSO), a regression method which imposes a penalty on the absolute value of the regression coefficients. LASSO presents some limitations since it can’t succeed in recovering the right set of features in multiple scenarios.
For this reason, the aim of this thesis is to perform a better feature selection using an iterative framework that, solving various LASSO problems of reduced dimension, obtains a ranking of the features from which the relevant ones can be retrieved.Candidate: Martina Sereni
Graduated April 2016
Iterative LASSO Framework for feature selection