In this paper, the combination of unreliable evidence sources is considered based on multiple criteria decision making (MCDM) model in intuitionistic fuzzy environment. In the intuitionistic fuzzy MCDM framework, evidence sources can be evaluated based on the ranking method between intuitionistic fuzzy numbers. A generalized discounting operation on unreliable evidence bodies is firstly proposed to deal with uncertain reliability factors. Then we mainly investigate the evaluation evidence reliability factors without prior knowledge. Our proposed evaluation method is based on the principle of self-assessment. It is implemented by the probabilistic comparison between intuitionistic fuzzy values. Our proposed evaluation method is independent to the dissimilarity measure between basic probability assignments represented by an evidential distance. Numerical examples demonstrate the performance of our proposed reliability evaluation method.

Uncertainty measure can supply a new viewpoint for analyzing knowledge conveyed by an Atanassov's intuitionistic fuzzy set (AIFS). So uncertainty measurement is a key topic in AIFS theory, analogous to the role of entropy in probability theory. After reviewing the existing measures of uncertainty (entropy) for AIFSs, we argue that the existing measures of uncertainty cannot capture all facets of uncertainty associated with an AIFS. Then we point out and justify that there are at least three kinds of uncertainty for an AIFS, namely non-specificity, fuzziness, and intuitionism. We provide formal measures of non-specificity, fuzziness, and intuitionism, together with their properties and proofs. Properties of the proposed non-specificity measure are especially investigated. Finally, a general uncertainty measure consisting of these three uncertainties is presented. Illustrative examples show that the proposed uncertainty measure is consistent with intuitive cognize, and it is more sensitive to changes of AIFSs. Moreover, the proposed uncertainty measure can also discriminate uncertainty hiding in classical sets. Thus, it provides an alternative way to construct unified uncertainty measures.

In this paper, the construction of similarity measures for Atanassov's intuitionistic fuzzy sets (AIFSs) is considered from the view of evidence theory. We define similarity measures for AIFSs in the framework of Dempster-Shafer evidence theory. The proposed similarity measures are applied to deal with pattern recognition and multiple criteria decision making problems. First, existing similarity measures for AIFSs are critically reviewed. Then we introduce the transformation from AIFSs to basic probability assignments (BPAs) in evidence theory. Based on Jousselme's distance measure and cosine similarity measure between BPAs, two similarity measures between AIFSs are proposed. A composite similarity measure is constructed following the proof of properties related to our proposed similarity measures. Then, we use some contrastive examples to illustrate that the proposed similarity measure between AIFSs can overcome the drawbacks of existing similarity measures. Finally, we apply the proposed similarity measures between AIFSs to deal with pattern recognition and multiple criteria decision making problems. It is demonstrated that our proposed similarity measures can provide compatible results compared to those results obtained based on previous measures.

This paper addresses the combination of unreliable evidence sources which provide uncertain information in the form of basic probability assignment (BPA) functions. We proposed a novel evidence combination rule based on credibility and non-specificity of belief functions. Following a review of all existing non-specificity measures in evidence theory, a non-specificity measure for evidence theory is discussed. It is claimed that the non-specificity degree of a BPA is related to its ability of pointing to one and only one element. Based on the difference between the largest belief grades and other belief grades, a non-specificity measure is defined. Properties of the proposed non-specificity measure are put forward and proved mathematically. Illustrative examples are employed to show the properties of non-specificity measure. After providing a procedure for the evaluation of evidence credibility, we propose a novel evidence combination rule. Numerical example and application in target identification are applied to demonstrate the performance of our proposed evidence combination rule.

Current Chinese national guidelines recommend routine screening for liver cancer in patients positive for HBsAg, irrespective of fibrosis status, age, or family history of liver cancer. We aim to evaluate whether the recommended screening strategy could reduce liver-cancer-specific mortality. We conducted a liver cancer mass screening trial in Xiaolan Town, Zhongshan City, China, among residents aged 35-64 years in 2012. All volunteers were offered serological testing for hepatitis B virus surface antigen (HBsAg). We proposed biannual screening using serum alpha-fetoprotein (AFP) and ultrasonography examination for subjects positive for HBsAg. Among 17,966 participants (26.2% of 68,510 eligible residents) who were free of liver cancer at baseline in 2012, we identified 57 incident cases of liver cancer within the first 4 years of follow-up (i.e., 43 among 2,848 HBsAg-positive participants and 14 among 15,118 HBsAg-negative participants), compared with 104 cases identified in non-participants (N=3D50,544). A total of 207 participants had the recommended number of ultrasonography examinations (every 6 months) during the screening period. Compared with cases identified from non-participants, the cases arising among participants were more likely to be at early stage and had better survival than those among non-participants. However, we did not observe a reduction in liver cancer-specific mortality rate among participants (relative risk=3D1.04, 95% confidence interval=3D0.68, 1.58, P=3D0.856). Our demonstration screening study does not show a reduction in liver cancer mortality within the first 4 years of follow-up according to current guidance in China, although long-term efficacy remains to be evaluated. Targeted surveillance among high-risk individuals as recommended by international guidelines, along with measures to improve compliance, should be evaluated in the Chinese population.=20