Implicit Multi-Feature Learning for Dynamic Time Series Prediction of the Impact of Institutions

Predicting the impact of research institutions is an important tool for decision makers, such as resource allocation for funding bodies. Despite significant effort of adopting quantitative indicators to measure the impact of research institutions, little is known that how the impact of institutions...

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Main Authors:	Xiaomei Bai, Fuli Zhang, Jie Hou, Feng Xia, Amr Tolba, Elsayed Elashkar
Format:	Article
Language:	English
Published:	IEEE 2017-01-01
Series:	IEEE Access
Subjects:	Scientific impact prediction feature selection machine learning scientometrics
Online Access:	https://ieeexplore.ieee.org/document/8010278/

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spelling	doaj-b3f07856dc354dd190d0c67dff7d87812021-03-29T20:05:46ZengIEEEIEEE Access2169-35362017-01-015163721638210.1109/ACCESS.2017.27391798010278Implicit Multi-Feature Learning for Dynamic Time Series Prediction of the Impact of InstitutionsXiaomei Bai0Fuli Zhang1Jie Hou2Feng Xia3https://orcid.org/0000-0002-8324-1859Amr Tolba4https://orcid.org/0000-0003-3439-6413Elsayed Elashkar5Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, School of Software, Dalian University of Technology, Dalian, ChinaLibrary, Anshan Normal University, Anshan, ChinaKey Laboratory for Ubiquitous Network and Service Software of Liaoning Province, School of Software, Dalian University of Technology, Dalian, ChinaKey Laboratory for Ubiquitous Network and Service Software of Liaoning Province, School of Software, Dalian University of Technology, Dalian, ChinaComputer Science Department, Community College, King Saud University, Riyadh, Saudi ArabiaAdministrative Sciences Department, Community College, King Saud University, Riyadh, Saudi ArabiaPredicting the impact of research institutions is an important tool for decision makers, such as resource allocation for funding bodies. Despite significant effort of adopting quantitative indicators to measure the impact of research institutions, little is known that how the impact of institutions evolves in time. Previous studies have focused on using the historical relevance scores of different institutions to predict potential future impact for these institutions. In this paper, we explore the factors that can drive the changes of the impact of institutions, finding that the impact of an institution, as measured by the number of the accepted papers of the institution, more is determined by the authors' influence of the institution. Geographic location of institution feature and state GDP can drive the changes of the impact of institutions. Identifying these features allows us to formulate a predictive model that integrates the effects of individual ability, location of institution, and state GDP. The model unveils the underlying factors driving the future impact of institutions, which can be used to accurately predict the future impact of institutions.https://ieeexplore.ieee.org/document/8010278/Scientific impactpredictionfeature selectionmachine learningscientometrics
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Xiaomei Bai Fuli Zhang Jie Hou Feng Xia Amr Tolba Elsayed Elashkar
spellingShingle	Xiaomei Bai Fuli Zhang Jie Hou Feng Xia Amr Tolba Elsayed Elashkar Implicit Multi-Feature Learning for Dynamic Time Series Prediction of the Impact of Institutions IEEE Access Scientific impact prediction feature selection machine learning scientometrics
author_facet	Xiaomei Bai Fuli Zhang Jie Hou Feng Xia Amr Tolba Elsayed Elashkar
author_sort	Xiaomei Bai
title	Implicit Multi-Feature Learning for Dynamic Time Series Prediction of the Impact of Institutions
title_short	Implicit Multi-Feature Learning for Dynamic Time Series Prediction of the Impact of Institutions
title_full	Implicit Multi-Feature Learning for Dynamic Time Series Prediction of the Impact of Institutions
title_fullStr	Implicit Multi-Feature Learning for Dynamic Time Series Prediction of the Impact of Institutions
title_full_unstemmed	Implicit Multi-Feature Learning for Dynamic Time Series Prediction of the Impact of Institutions
title_sort	implicit multi-feature learning for dynamic time series prediction of the impact of institutions
publisher	IEEE
series	IEEE Access
issn	2169-3536
publishDate	2017-01-01
description	Predicting the impact of research institutions is an important tool for decision makers, such as resource allocation for funding bodies. Despite significant effort of adopting quantitative indicators to measure the impact of research institutions, little is known that how the impact of institutions evolves in time. Previous studies have focused on using the historical relevance scores of different institutions to predict potential future impact for these institutions. In this paper, we explore the factors that can drive the changes of the impact of institutions, finding that the impact of an institution, as measured by the number of the accepted papers of the institution, more is determined by the authors' influence of the institution. Geographic location of institution feature and state GDP can drive the changes of the impact of institutions. Identifying these features allows us to formulate a predictive model that integrates the effects of individual ability, location of institution, and state GDP. The model unveils the underlying factors driving the future impact of institutions, which can be used to accurately predict the future impact of institutions.
topic	Scientific impact prediction feature selection machine learning scientometrics
url	https://ieeexplore.ieee.org/document/8010278/
work_keys_str_mv	AT xiaomeibai implicitmultifeaturelearningfordynamictimeseriespredictionoftheimpactofinstitutions AT fulizhang implicitmultifeaturelearningfordynamictimeseriespredictionoftheimpactofinstitutions AT jiehou implicitmultifeaturelearningfordynamictimeseriespredictionoftheimpactofinstitutions AT fengxia implicitmultifeaturelearningfordynamictimeseriespredictionoftheimpactofinstitutions AT amrtolba implicitmultifeaturelearningfordynamictimeseriespredictionoftheimpactofinstitutions AT elsayedelashkar implicitmultifeaturelearningfordynamictimeseriespredictionoftheimpactofinstitutions
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Implicit Multi-Feature Learning for Dynamic Time Series Prediction of the Impact of Institutions

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