Identifying Economic Factors of Local Government Transparency: Based on Apriori and LSTM-Attention

DOI

10.2991/978-94-6463-222-4_22How to use a DOI?

Keywords

LSTM-Attention model; Features extraction; Deep learning; Apriori; Economy

Abstract

This study examines the impact of economic factors on local government transparency and proposes a prediction framework called AP-LSTM, which uses feature extraction and Apriori to select highly correlated economic factors as input for the LSTM-Attention network. The proposed method is validated using historical data from Shandong Province. Results show an interval correspondence between economic factors and transparency, and the prediction accuracy of the network is improved with the feature extraction method. The LSTM-Attention network’s prediction results have an important influence on rank derivation and benchmark improvement for local government transparency.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Mingle Zhou
AU  - Ran Wang
AU  - Delong Han
AU  - Min Li
PY  - 2023
DA  - 2023/08/28
TI  - Identifying Economic Factors of Local Government Transparency: Based on Apriori and LSTM-Attention
BT  - Proceedings of the 2023 2nd International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2023)
PB  - Atlantis Press
SP  - 225
EP  - 232
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-222-4_22
DO  - 10.2991/978-94-6463-222-4_22
ID  - Zhou2023
ER  -