Bagging of Duo Output Neural Networks for Single Output Regression Problem

This paper presents an approach to the single output regression problem using ensemble of duo output neural networks based on bagging technique. Each component in the ensemble consists of a pair of duo output neural networks. The first neural network is trained to provide duo outputs which are a pai...

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Main Authors: Somkid Amornsamankul, Pawalai Kraipeerapun
Other Authors: Mahidol University
Format: Conference or Workshop Item
Published: 2018
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Online Access:https://repository.li.mahidol.ac.th/handle/123456789/29028
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spelling th-mahidol.290282018-09-24T16:01:32Z Bagging of Duo Output Neural Networks for Single Output Regression Problem Somkid Amornsamankul Pawalai Kraipeerapun Mahidol University Ramkhamhaeng University Computer Science Engineering This paper presents an approach to the single output regression problem using ensemble of duo output neural networks based on bagging technique. Each component in the ensemble consists of a pair of duo output neural networks. The first neural network is trained to provide duo outputs which are a pair of truth and falsity values whereas the second neural network provides a pair of falsity and truth values. The target outputs used to train the second network are organized in reverse order of the first network. For the former neural network, the truth and non-falsity outputs are used to created the average truth output. For the later neural network, the falsity and non-truth outputs are used to provide the average falsity output. In order to combine outputs from components in the ensemble, the simple averaging and the dynamic weighted averaging techniques are used. The weight is created based on the difference between the truth and non-falsity values. The proposed approach has been tested with three benchmarking VCI data sets, which are housing, concrete compressive strength, and computer hardware. The proposed ensemble methods improves the performance as compared to the traditional ensemble of neural networks, the ensemble of complementary neural networks, and the ensemble of support vector machine with linear, polynomial, and radial basis function kernels. © 2010 IEEE. 2018-09-24T08:57:50Z 2018-09-24T08:57:50Z 2010-01-01 Conference Paper Proceedings - 2010 3rd IEEE International Conference on Computer Science and Information Technology, ICCSIT 2010. Vol.7, (2010), 135-139 10.1109/ICCSIT.2010.5564576 2-s2.0-77958594804 https://repository.li.mahidol.ac.th/handle/123456789/29028 Mahidol University SCOPUS https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=77958594804&origin=inward
institution Mahidol University
building Mahidol University Library
continent Asia
country Thailand
Thailand
content_provider Mahidol University Library
collection Mahidol University Institutional Repository
topic Computer Science
Engineering
spellingShingle Computer Science
Engineering
Somkid Amornsamankul
Pawalai Kraipeerapun
Bagging of Duo Output Neural Networks for Single Output Regression Problem
description This paper presents an approach to the single output regression problem using ensemble of duo output neural networks based on bagging technique. Each component in the ensemble consists of a pair of duo output neural networks. The first neural network is trained to provide duo outputs which are a pair of truth and falsity values whereas the second neural network provides a pair of falsity and truth values. The target outputs used to train the second network are organized in reverse order of the first network. For the former neural network, the truth and non-falsity outputs are used to created the average truth output. For the later neural network, the falsity and non-truth outputs are used to provide the average falsity output. In order to combine outputs from components in the ensemble, the simple averaging and the dynamic weighted averaging techniques are used. The weight is created based on the difference between the truth and non-falsity values. The proposed approach has been tested with three benchmarking VCI data sets, which are housing, concrete compressive strength, and computer hardware. The proposed ensemble methods improves the performance as compared to the traditional ensemble of neural networks, the ensemble of complementary neural networks, and the ensemble of support vector machine with linear, polynomial, and radial basis function kernels. © 2010 IEEE.
author2 Mahidol University
author_facet Mahidol University
Somkid Amornsamankul
Pawalai Kraipeerapun
format Conference or Workshop Item
author Somkid Amornsamankul
Pawalai Kraipeerapun
author_sort Somkid Amornsamankul
title Bagging of Duo Output Neural Networks for Single Output Regression Problem
title_short Bagging of Duo Output Neural Networks for Single Output Regression Problem
title_full Bagging of Duo Output Neural Networks for Single Output Regression Problem
title_fullStr Bagging of Duo Output Neural Networks for Single Output Regression Problem
title_full_unstemmed Bagging of Duo Output Neural Networks for Single Output Regression Problem
title_sort bagging of duo output neural networks for single output regression problem
publishDate 2018
url https://repository.li.mahidol.ac.th/handle/123456789/29028
_version_ 1763491607502389248