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Reseach Article

Coupled Kernel Ensemble Regression

by Dickson Keddy Wornyo, Elias Nii Noi Ocquaye, Bright Bediako-Kyeremeh
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 181 - Number 34
Year of Publication: 2018
Authors: Dickson Keddy Wornyo, Elias Nii Noi Ocquaye, Bright Bediako-Kyeremeh
10.5120/ijca2018918278

Dickson Keddy Wornyo, Elias Nii Noi Ocquaye, Bright Bediako-Kyeremeh . Coupled Kernel Ensemble Regression. International Journal of Computer Applications. 181, 34 ( Dec 2018), 1-8. DOI=10.5120/ijca2018918278

@article{ 10.5120/ijca2018918278,
author = { Dickson Keddy Wornyo, Elias Nii Noi Ocquaye, Bright Bediako-Kyeremeh },
title = { Coupled Kernel Ensemble Regression },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2018 },
volume = { 181 },
number = { 34 },
month = { Dec },
year = { 2018 },
issn = { 0975-8887 },
pages = { 1-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume181/number34/30207-2018918278/ },
doi = { 10.5120/ijca2018918278 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:08:04.666337+05:30
%A Dickson Keddy Wornyo
%A Elias Nii Noi Ocquaye
%A Bright Bediako-Kyeremeh
%T Coupled Kernel Ensemble Regression
%J International Journal of Computer Applications
%@ 0975-8887
%V 181
%N 34
%P 1-8
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, the concept of kernel ensemble regression scheme is enhanced considering the absorption of multiple kernel regrssors into a unified ensemble regression framework simultaneously and coupled by minimizing total loss of ensembles in Reproducing kernel Hilbert Space. By this, one kernel regressor with more accurate fitting precession on data can automatically obtain bigger weight, which leads to a better overall ensemble performance. Comparing several single and ensemble regression methods such as Gradient Boosting, Support Vector Regression, Ridge Regression, Tree Regression and Random Forest with our proposed method, the experimental results of the proposed model indicates the highest performances in terms with regression and classification tasks using several UCI dataset.

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Index Terms

Computer Science
Information Sciences

Keywords

Ensemble regression Multi-kernel learning Kernel regression