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An ensemble learning method based on deep neural network and group decision making - ScienceDirect
Source: sciencedirect.com
Article summary:
1. An ensemble learning method based on deep neural network and group decision making (DNN-GDM-EL) is proposed, which uses deep neural networks (DNNs) to generate individual learners and exploits group decision making (GDM) to combine these learners.
2. A GDM model is established based on Bayesian theory which can reflect the complex relationship among the class of image, prior knowledge and output of DNN, and a GDM method based on TOPSIS is applied to solve this problem.
3. An aggregation method based on 2-additive generalized Shapley AIVIFCA (2AGSAIVIFCA) operator is used to calculate the weights of DMs by fusing these matrixes, and state transition algorithm (STA) is applied to obtain the optimal weights of alternative's attributes.
Article analysis:
The article “An ensemble learning method based on deep neural network and group decision making” provides an overview of a proposed ensemble learning method that combines deep neural networks with group decision making for image classification tasks. The article presents a comprehensive description of the proposed approach, including its theoretical background, implementation details, and evaluation results.
The article appears to be well researched and reliable in terms of its content. It provides a detailed explanation of the theoretical background behind the proposed approach as well as its implementation details. Furthermore, it includes evaluation results from three public datasets as well as a real industrial problem that demonstrate the effectiveness and superiority of the proposed approach compared to other typical EL methods.
However, there are some potential biases in the article that should be noted. For example, while it does provide an overview of related work in this field, it does not explore any counterarguments or alternative approaches that could potentially be more effective than what is presented in this article. Additionally, while it does provide evaluation results from multiple datasets, it does not provide any evidence for why these datasets were chosen or how they are representative of real-world applications. Finally, while it does discuss potential risks associated with using this approach such as overfitting or data leakage issues due to using multiple models simultaneously, it does not provide any suggestions for how these risks can be mitigated or avoided altogether.
In conclusion, while this article provides a comprehensive overview of an ensemble learning approach combining deep neural networks with group decision making for image classification tasks, there are some potential biases that should be noted when evaluating its trust