篇名 |
Multi-source Heterogeneous Data Fusion Model Based on FC-SAE
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並列篇名 | Multi-source Heterogeneous Data Fusion Model Based on FC-SAE |
作者 | Hong Zhang、Kun Jiang、Chuanqi Cheng、Jie Cao、Wenyue Zhang |
英文摘要 | Multi-source heterogeneous data has different degrees of data correlation or data conflict. How to fuse this data and fully mine its inherent meanings to obtain more accurate decision information is a problem that needs to be solved urgently. This paper proposes a multi-source heterogeneous data fusion model based on fully connected layers and sparse autoencoders (short for FC-SAE) to solve the above problem. This model can effectively improve the time series forecasting performance compared with the traditional time series forecasting model. The MAE value is reduced by 4.4% and the RMSE value is reduced by 3.7%. In terms of fusion strategy, the method that uses the sparse autoencoder as the fusion strategy reduces the MAE value by 1.7% and the RMSE value by 2.3% compared with the method that uses the fully connected layer as the fusion strategy.
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起訖頁 | 1473-1481 |
關鍵詞 | Multi-source heterogeneous、Data fusion、Deep learning、SAE、FC |
刊名 | 網際網路技術學刊 |
期數 | 202212 (23:7期) |
出版單位 | 台灣學術網路管理委員會 |
DOI |
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QR Code | |
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