dc.contributor.author | Zhao, L | |
dc.contributor.author | Chen, Z | |
dc.contributor.author | Hu, Y | |
dc.contributor.author | Min, G | |
dc.contributor.author | Jiang, Z | |
dc.date.accessioned | 2017-02-14T11:29:00Z | |
dc.date.issued | 2016-08-23 | |
dc.description.abstract | With the rapidly increasing popularity of economic activities, a large amount of economic data is being collected. Although
such data offers super opportunities for economic analysis, its low-quality, high-dimensionality and huge-volume pose great challenges
on efficient analysis of economic big data. The existing methods have primarily analyzed economic data from the perspective of
econometrics, which involves limited indicators and demands prior knowledge of economists. When embracing large varieties of
economic factors, these methods tend to yield unsatisfactory performance. To address the challenges, this paper presents a new
framework for efficient analysis of high-dimensional economic big data based on innovative distributed feature selection. Specifically,
the framework combines the methods of economic feature selection and econometric model construction to reveal the hidden patterns
for economic development. The functionality rests on three pillars: (i) novel data pre-processing techniques to prepare high-quality
economic data, (ii) an innovative distributed feature identification solution to locate important and representative economic indicators
from multidimensional data sets, and (iii) new econometric models to capture the hidden patterns for economic development. The
experimental results on the economic data collected in Dalian, China, demonstrate that our proposed framework and methods have
superior performance in analyzing enormous economic data. | en_GB |
dc.description.sponsorship | This work is supported by National Natural Science Foundation
Project of China (U1301253), Science and Technology
Planning Key Project of Guangdong Province, China
(2015B010110006) and Research Office of Dalian Government
in China. | en_GB |
dc.identifier.citation | DOI: 10.1109/TBDATA.2016.2601934 | en_GB |
dc.identifier.doi | 10.1109/TBDATA.2016.2601934 | |
dc.identifier.uri | http://hdl.handle.net/10871/25841 | |
dc.language.iso | en | en_GB |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_GB |
dc.rights | (c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. | en_GB |
dc.subject | feature selection | en_GB |
dc.subject | big data | en_GB |
dc.subject | subtractive clustering | en_GB |
dc.subject | collaborative theory | en_GB |
dc.subject | economy | en_GB |
dc.subject | urbanization | en_GB |
dc.title | Distributed feature selection for efficient economic big data analysis | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2017-02-14T11:29:00Z | |
dc.identifier.issn | 2332-7790 | |
dc.description | This is the author accepted manuscript. The final version is available from the publisher via the DOI in this record. | en_GB |
dc.identifier.journal | IEEE Transactions on Big Data | en_GB |