Artificial Intelligence
A pseudo-nearest-neighbor approach for missing data recovery on Gaussian random data sets
Pattern Recognition Letters
Combining belief functions based on distance of evidence
Decision Support Systems
Data Mining
Top 10 algorithms in data mining
Knowledge and Information Systems
CSMC: A combination strategy for multi-class classification based on multiple association rules
Knowledge-Based Systems
Can reputation migrate? On the propagation of reputation in multi-context communities
Knowledge-Based Systems
Rough set theory based on two universal sets and its applications
Knowledge-Based Systems
Modeling contaminant intrusion in water distribution networks: A new similarity-based DST method
Expert Systems with Applications: An International Journal
Induced generalized intuitionistic fuzzy operators
Knowledge-Based Systems
A hybrid particle swarm optimization approach for clustering and classification of datasets
Knowledge-Based Systems
A new linguistic MCDM method based on multiple-criterion data fusion
Expert Systems with Applications: An International Journal
A new fuzzy dempster MCDM method and its application in supplier selection
Expert Systems with Applications: An International Journal
The quasi-arithmetic intuitionistic fuzzy OWA operators
Knowledge-Based Systems
Evaluating Sensor Reliability in Classification Problems Based on Evidence Theory
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Deriving Evidence Theoretical Functions in Multivariate Data Spaces: A Systematic Approach
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Connectionist-based Dempster-Shafer evidential reasoning for data fusion
IEEE Transactions on Neural Networks
Knowledge-Based Systems
Short Communication: A new optimal consensus method with minimum cost in fuzzy group decision
Knowledge-Based Systems
A biologically inspired solution for fuzzy shortest path problems
Applied Soft Computing
Environmental impact assessment based on D numbers
Expert Systems with Applications: An International Journal
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The Dempster-Shafer evidence theory (D-S theory) is one of the primary tools for knowledge representation and uncertain reasoning, and has been widely used in many information fusion systems. However, how to determine the basic probability assignment (BPA), which is the main and first step in D-S theory, is still an open issue. In this paper, based on the normal distribution, a method to obtain BPA is proposed. The training data are used to build a normal distribution-based model for each attribute of the data. Then, a nested structure BPA function can be constructed, using the relationship between the test data and the normal distribution model. A normality test and normality transformation are integrated into the proposed method to handle non-normal data. The missing attribute values in datasets are addressed as ignorance in the framework of the evidence theory. Several benchmark pattern classification problems are used to demonstrate the proposed method and to compare against existing methods. Experiments provide encouraging results in terms of classification accuracy, and the proposed method is seen to perform well without a large amount of training data.