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Estimation of regression-based model with bulk noisy data

Main Author: Chanintorn Jittawiriyanukoon
Format: Article
Terbitan: , 2019
Subjects:
Bulk noise
Classification
Estimation
Mean square error
Noisy and missing data
Regression-based model
Online Access: https://zenodo.org/record/4066372
ctrlnum 4066372
fullrecord <?xml version="1.0"?> <dc schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><creator>Chanintorn Jittawiriyanukoon</creator><date>2019-10-01</date><description>The bulk noise has been provoking a contributed data due to a communication network with a tremendously low signal to noise ratio. An appreciated method for revising massive noise of individuals through information theory is widely discussed. One of the practical applications of this approach for bulk noise estimation is analyzed using intelligent automation and machine learning tools, dealing the case of bulk noise existence or nonexistence. A regression-based model is employed for the investigation and experiment. Estimation for the practical case with bulk noisy datasets is proposed. The proposed method applies slice-and-dice technique to partition a body of datasets down into slighter portions so that it can be carried out. The average error, correlation, absolute error and mean square error are computed to validate the estimation. Results from massive online analysis will be verified with data collected in the following period. In many cases, the prediction with bulk noisy data through MOA simulation reveals Random Imputation minimizes the average error.</description><identifier>https://zenodo.org/record/4066372</identifier><identifier>10.11591/ijece.v9i5.pp3649-3656</identifier><identifier>oai:zenodo.org:4066372</identifier><rights>info:eu-repo/semantics/openAccess</rights><rights>https://creativecommons.org/licenses/by/4.0/legalcode</rights><source>International Journal of Electrical and Computer Engineering (IJECE) 9(5) 3649-3656</source><subject>Bulk noise</subject><subject>Classification</subject><subject>Estimation</subject><subject>Mean square error</subject><subject>Noisy and missing data</subject><subject>Regression-based model</subject><title>Estimation of regression-based model with bulk noisy data</title><type>Journal:Article</type><type>Journal:Article</type><recordID>4066372</recordID></dc>
format Journal:Article
Journal
author Chanintorn Jittawiriyanukoon
title Estimation of regression-based model with bulk noisy data
publishDate 2019
topic Bulk noise
Classification
Estimation
Mean square error
Noisy and missing data
Regression-based model
url https://zenodo.org/record/4066372
contents The bulk noise has been provoking a contributed data due to a communication network with a tremendously low signal to noise ratio. An appreciated method for revising massive noise of individuals through information theory is widely discussed. One of the practical applications of this approach for bulk noise estimation is analyzed using intelligent automation and machine learning tools, dealing the case of bulk noise existence or nonexistence. A regression-based model is employed for the investigation and experiment. Estimation for the practical case with bulk noisy datasets is proposed. The proposed method applies slice-and-dice technique to partition a body of datasets down into slighter portions so that it can be carried out. The average error, correlation, absolute error and mean square error are computed to validate the estimation. Results from massive online analysis will be verified with data collected in the following period. In many cases, the prediction with bulk noisy data through MOA simulation reveals Random Imputation minimizes the average error.
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