Rough set theory and belief function theory, two popular mathematical frameworks for
uncertainty representation, have been widely applied in different settings and contexts …

Uncertainty and imprecision accompany the world we live in and occur in almost every
event. How to better interpret and manage uncertainty and imprecision play a vital role in …

Performance metrics (error measures) are vital components of the evaluation frameworks in
various fields. The intention of this study was to overview of a variety of performance metrics …

Aim/Purpose: The aim of this study was to analyze various performance metrics and
approaches to their classification. The main goal of the study was to develop a new typology …

Evidence theory, also called belief function theory, provides an efficient tool to represent and
combine uncertain information for pattern classification. Evidence combination can be …

In pattern classification problem, different classifiers learnt using different training data can
provide more or less complementary knowledge, and the combination of classifiers is …

How to manage conflict is still an open issue in Dempster–Shafer (DS) evidence theory. The
conflict coefficient k in DS evidence theory cannot represent conflict reasonably, especially …

Classifier fusion is an efficient strategy to improve the classification performance for the
complex pattern recognition problem. In practice, the multiple classifiers to combine can …

在对证据理论的建模, 推理, 决策到评估各层面最新进展梳理的基础上, 对证据理论现有研究中
存在的一些问题, 混淆和误解结合仿真算例进行了分析和探讨, 包括证据理论与概率论的关系 …

Evidence theory is an effective methodology for modeling and processing uncertainty that
has been widely applied in various fields. In evidence theory, a number of distance …