Determination of Evidence Weights Based on Convolutional Neural Network for Classification Problem

Abstract

The weighted average is an efficient way to address conflicting evidence combination in the Dempster-Shafer evidence theory. However, it is an open issue how to determine the evidence weights reasonably. Although many traditional conflicting evidence combination solutions based on evidence distance or entropy have been put forward, the evidence weights are determined with a single aspect, and no comprehensive consideration of other useful information affects the weights. Thus, it does not ensure that determination of weights is the most reasonable. By introducing deep learning into conflicting evidence combination, this paper proposes a comprehensive method for determining the evidence weights based on a convolutional neural network. Taking the evidence as the network input and the corresponding weight as the output, it utilizes convolutional neural network to fully mine potentially useful information that affects the evidence weights, in order to determine the weights comprehensively. Additionally, we define a weight loss function. The weights are continuously optimized through back propagation and achieve the optimal when the weight loss function value is the minimum. Classification experimental results demonstrate that the proposed method outperforms traditional ones based on evidence distance or entropy and can be flexibly extended to other application fields as a decision-making fusion method.