Applied Intelligence, ( ISI ), Year (2018-11)

Title : ( RELF: Robust Regression Extended with Ensemble Loss Function )

Authors: hamideh hajiabadi , Reza Monsefi , Hadi Sadoghi Yazdi ,

Citation: BibTeX | EndNote

Abstract

Ensemble techniques are powerful approaches that combine several weak learners to build a stronger one. As a meta-learning framework, ensemble techniques can easily be applied to many machine learning methods. Inspired by ensemble techniques, in this paper we propose an ensemble loss functions applied to a simple regressor. We then propose a half-quadratic learning algorithm in order to find the parameter of the regressor and the optimal weights associated with each loss function. Moreover, we show that our proposed loss function is robust in noisy environments. For a particular class of loss functions, we show that our proposed ensemble loss function is Bayes consistent and robust. Experimental evaluations on several data sets demonstrate that the our proposed ensemble loss function significantly improves the performance of a simple regressor in comparison with state-of-the-art methods.

Keywords

Loss function Ensemble methods Bayes Consistent Loss function Robustness
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@article{paperid:1071077,
author = {Hajiabadi, Hamideh and Monsefi, Reza and Sadoghi Yazdi, Hadi},
title = {RELF: Robust Regression Extended with Ensemble Loss Function},
journal = {Applied Intelligence},
year = {2018},
month = {November},
issn = {0924-669X},
keywords = {Loss function Ensemble methods Bayes Consistent Loss function Robustness},
}

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%0 Journal Article
%T RELF: Robust Regression Extended with Ensemble Loss Function
%A Hajiabadi, Hamideh
%A Monsefi, Reza
%A Sadoghi Yazdi, Hadi
%J Applied Intelligence
%@ 0924-669X
%D 2018

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