Deep Forest Regression Based on Dynamic State Transition Optimization Algorithm

As a deep algorithm of non-neural network structure, deep forest regression (DFR) can be used to build soft measuring models of difficult-to-measure key parameters. However, as a kind of deep learning, the optimization of hyperparameters has become an inevitable problem in DFR. To solve above proble...

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Vydáno v:Chinese Automation Congress (Online) s. 3786 - 3791
Hlavní autoři: Xia, Heng, Tang, Jian, Qiao, Junfei
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 06.11.2020
Témata:
Data models
deep forest regression
dynamic state transition algorithm
Forestry
Heuristic algorithms
hyper-parameters optimization
Optimization
Stochastic processes
Support vector machines
Training
ISSN:2688-0938
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Shrnutí:As a deep algorithm of non-neural network structure, deep forest regression (DFR) can be used to build soft measuring models of difficult-to-measure key parameters. However, as a kind of deep learning, the optimization of hyperparameters has become an inevitable problem in DFR. To solve above problem, an improved dynamic state transition algorithm (DSTA) is used to optimize the hyper-parameters of the model. To achieved more accurate optimization process, the error change rate is used to fine-tuning the state factor during the iteration process, which is further improved with gradient-based refinement. Finally, simulation experiments are performed on the benchmark data set, and satisfactory simulation results show the effectiveness of the proposed approach.
ISSN:2688-0938
DOI:10.1109/CAC51589.2020.9327803