An ensemble learning method based on deep neural network and group decision making

Ensemble learning (EL) method which has high potential to improve the performance of single image classification model can be constructed in two steps: one is the generation of weak learners; the other is the combination of these learners. In this paper, 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. DNNs have demonstrated remarkable ability for image classification due to the powerful feature extraction ability. To ensure the diversity and accuracy, many different DNNs are used to generate individual learners. Furthermore, the individual learners are regarded as decision-makers (DMs), the categories are seen as alternatives, and the GDM aims to find an optimal alternative considering various suggestions of DMs. Specifically, 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. Next, the index matrix consisted of DM's attributes is proposed, and an aggregation method based on 2-additive generalized Shapley AIVIFCA (2AGSAIVIFCA) operator is used to calculate the weights of DMs by fusing these matrixes. Further, state transition algorithm (STA) is applied to obtain the optimal weights of alternative's attributes. The effectiveness and superiority are verified in three public data sets and a real industrial problem by comparing DNN-GDM-EL method with other typical EL methods. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

This paper proposes an ensemble learning method based on deep neural network and group decision making. Multiple deep neural networks are used to generate individual learners, and group decision making is applied to find the optimal alternative. Experimental results demonstrate the effectiveness and superiority of this method in image classification.