Ensemble learning via higher order singular value decomposition for integrating data and classifier fusion in water quality monitoring

Zhibin Sun, Ni Bin Chang, Chi Farn Chen, Chandan Mostafiz, Wei Gao

研究成果: 雜誌貢獻期刊論文同行評審

4 引文 斯高帕斯(Scopus)

摘要

Current multimodal learning in smart feature extraction and classification has reshaped the landscape of remote sensing. The recent developments in smart feature extraction mainly rely on different machine learning and data mining algorithms as powerful classifiers to improve prediction accuracy. This article presents an innovative ensemble learning algorithm for integrated data and classifier fusion via higher order singular value decomposition (IDCF-HOSVD). Based on the fused data, different analytical, semiempirical, and empirical classifiers can be selected and applied to perform information retrieval that can be further synergized via a tensor flow based feature extraction scheme over the classifier space. When preserving core fused image patterns via HOSVD, the final step of IDCF-HOSVD helps rank the contributions from different classifiers via nonlinear correlation information entropy. Practical implementation of the IDCF-HOSVD algorithm was assessed through its application to map the seasonal water quality conditions in Lake Nicaragua, Central America.

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文章編號09345352
頁(從 - 到)3345-3360
頁數16
期刊IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
14
DOIs
出版狀態已出版 - 2021

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