Robust least angle regression and LASSO using bootstrap aggregation (real case study: Iranian poultry farms) Online publication date: Wed, 19-Apr-2017
by Reza Behmanesh; Abbas Saghaei
International Journal of Industrial and Systems Engineering (IJISE), Vol. 26, No. 2, 2017
Abstract: Over the past ten years, many researchers and practitioners have concentrated on the topic of least angle regression, LASSO and forward stagewise (LARS) considerably instead of other subset selection models such as forward and backward stepwise. This model selection algorithm is useful and less greedy in comparison with traditional approaches similar forward stepwise. In this study, we present a taxonomy of all researches that pertains to LARS, and a review of this literature including extensions of it. Next, we introduce a robust LARS by applying bootstrap aggregation to reduce the variance of LARS model. For this purpose, we employ real world case of Iranian poultry farms so as to illustrate our proposed models. The results indicate the proposed hybrid model predicts more accurate compared to classic LARS algorithm. At last, we suggest opportunities for future research in this area.
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