Classifying vaguely labeled data based on evidential fusion

An Evidential Software Risk Evaluation Model

Software risk management is an important factor in ensuring software quality. Therefore, software risk assessment has become a significant and challenging research area. The aim of this study is to establish a data-driven software risk assessment model named DDERM. In the proposed model, experts’ risk assessments of probability and severity can be transformed into basic probability assignments (BPAs). Deng entropy was used to measure the uncertainty of the evaluation and to calculate the criteria weights given by experts. In addition, the adjusted BPAs were fused using the rules of Dempster–Shafer evidence theory (DST). Finally, a risk matrix was used to get the risk priority. A case application demonstrates the effectiveness of the proposed method. The proposed risk modeling framework is a novel approach that provides a rational assessment structure for imprecision in software risk and is applicable to solving similar risk management problems in other domains.

Maximum Entropy (MaxEnt) Based DEMATEL and Its Application in Emergency Management

Since DEMATEL can visualize the structure of complex causal relationships, it is widely used in decision making. One of the important steps in DEMATEL is normalization, and it has received a lot of attention in recent years. Maximum entropy is a universal principle, and it is an effective tool for determining the amount of information existed in evidence. In this paper, maximum entropy based DEMATEL, named as MaxEnt-DEMETEL is proposed, the greatest contribution in this paper is the use of maximum entropy principle to determine the normalized direct influence matrix, which makes it possible to obtain the normalized matrix with minimal information loss. Emergency management is illustrated to show the superiority of the proposed method.