Results 11 to 20 of about 5,670,665 (286)
Consonant approximations of belief functions
International Journal of Approximate Reasoning, 1990 Based on a recently introduced definition of inclusion between random sets, a general notion of approximation of a belief function by some other set-function is introduced. Viewing a fuzzy set as a consonant random set, it is shown how to construct fuzzy sets that may act as approximations of belief functions. Two kinds of approximations are considered:
Henri Prade
exaly +4 more sources
Loopy belief propagation and probabilistic image processing [PDF]
, 2003 Estimation of hyperparameters by maximization of the marginal likelihood in probabilistic image processing is investigated by using the cluster variation method.
Inoue, J., Tanaka, K., Titterington, M.
core +8 more sources
Belief-Logic Conflict Resolution in Syllogistic Reasoning: Inspection-Time Evidence for a Parallel-Process Model [PDF]
, 2008 An experiment is reported examining dual-process models of belief bias in syllogistic reasoning using a problem complexity manipulation and an inspection-time method to monitor processing latencies for premises and conclusions.
Stupple, Edward J. N. +3 more
core +5 more sources
Entropy, 2022 As well-known machine learning methods, decision trees are widely applied in classification and recognition areas. In this paper, with the uncertainty of labels handled by belief functions, a new decision tree method based on belief entropy is proposed ...
Kangkai Gao, Yong Wang, Liyao Ma
doaj +1 more source
Feature Selection for Interval-Valued Data Based on D-S Evidence Theory
IEEE Access, 2021 Feature selection is one basic and critical technology for data mining, especially in current “big data era”. Rough set theory (RST) is sensitive to noise in feature selection due to the strict condition of equivalence relation.
Yichun Peng, Qinli Zhang
doaj +1 more source
Mathematics, 2020 The Dempster–Shafer evidence theory has been widely applied in the field of information fusion. However, when the collected evidence data are highly conflicting, the Dempster combination rule (DCR) fails to produce intuitive results most of the time.
Dingyi Gan, Bin Yang, Yongchuan Tang
doaj +1 more source
Credal Transfer Learning With Multi-Estimation for Missing Data
IEEE Access, 2020 Transfer learning (TL) has grown popular in recent years. It is effective to improve the classification accuracy in the target domain by using the training knowledge in the related domain (called source domain).
Zongfang Ma +4 more
doaj +1 more source
Pignistic Belief Transform: A New Method of Conflict Measurement
IEEE Access, 2020 To measure conflict between two basic probability assignment functions plays the key role of conflict management in Dempster-shafer evidence theory. In this paper, a new conflict measure is proposed.
Qixuan Cai, Xiaozhuan Gao, Yong Deng
doaj +1 more source
A Reliability-Based Multisensor Data Fusion with Application in Target Classification
Sensors, 2020 The theory of belief functions has been extensively utilized in many practical applications involving decision making. One such application is the classification of target based on the pieces of information extracted from the individual attributes ...
Gabriel Awogbami, Abdollah Homaifar
doaj +1 more source
A New Evaluation Methodology for Quality Goals Extended by D Number Theory and FAHP
Information, 2020 Evaluation of quality goals is an important issue in process management, which essentially is a multi-attribute decision-making (MADM) problem. The process of assessment inevitably involves uncertain information. The two crucial points in an MADM problem
Hongming Mo
doaj +1 more source