On the Dempster-Shafer framework and new combination rules
Information Sciences: an International Journal
The Combination of Evidence in the Transferable Belief Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust reasoning: integrating rule-based and similarity-based reasoning
Artificial Intelligence
A new class of fuzzy implications, axioms of fuzzy implication revisited
Fuzzy Sets and Systems
Modeling uncertainty using partial information
Information Sciences: an International Journal
Neuro-fuzzy approach versus rough-set inspired methodology for intelligent decision support
Information Sciences—Informatics and Computer Science: An International Journal
Combining belief functions when evidence conflicts
Decision Support Systems
Ranking fuzzy numbers using ω-weighted valuations
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Current Approaches to Handling Imperfect Information in Data and Knowledge Bases
IEEE Transactions on Knowledge and Data Engineering
Investment using technical analysis and fuzzy logic
Fuzzy Sets and Systems - Special issue: Optimization and decision support systems
Fuzzy modeling of manufacturing and logistic systems
Mathematics and Computers in Simulation
Adequacy of training data for evolutionary mining of trading rules
Decision Support Systems - Special issue: Data mining for financial decision making
Applying rough sets to market timing decisions
Decision Support Systems - Special issue: Data mining for financial decision making
A preference aggregation method through the estimation of utility intervals
Computers and Operations Research
Information Sciences: an International Journal
Analyzing the combination of conflicting belief functions
Information Fusion
Information Sciences: an International Journal
A DS-AHP approach for multi-attribute decision making problem with incomplete information
Expert Systems with Applications: An International Journal
Two-objective method for crisp and fuzzy interval comparison in optimization
Computers and Operations Research
Combining uncertainty and imprecision in models of medical diagnosis
Information Sciences: an International Journal
On the evidential reasoning algorithm for multiple attribute decision analysis under uncertainty
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Belief rule-base inference methodology using the evidential reasoning Approach-RIMER
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Optimization Models for Training Belief-Rule-Based Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Designing and learning of adjustable quasi-triangular norms with applications to neuro-fuzzy systems
IEEE Transactions on Fuzzy Systems
Assessment of E-Commerce security using AHP and evidential reasoning
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A stock trading expert system based on the rule-base evidential reasoning using Level 2 Quotes
Expert Systems with Applications: An International Journal
Using a fuzzy association rule mining approach to identify the financial data association
Expert Systems with Applications: An International Journal
A method for comparing intervals with interval bounds
PPAM'11 Proceedings of the 9th international conference on Parallel Processing and Applied Mathematics - Volume Part II
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Evidential location estimation for events detected in Twitter
Proceedings of the 7th Workshop on Geographic Information Retrieval
Hybrid Kansei-SOM model using risk management and company assessment for stock trading
Information Sciences: an International Journal
Hi-index | 12.06 |
The synthesis of fuzzy logic and methods of the Dempster-Shafer theory (the so-called rule-base evidential reasoning) is proved to be a powerful tool for building expert and decision making systems. Nevertheless, there are two limitations of such approaches that reduce their ability to deal with uncertainties the decision makers often meet in practice. The first limitation is that in the framework of known approaches to the rule-base evidential reasoning, a degree of belief can be assigned only to a particular hypothesis, not to a group of them, whereas an assignment of a belief mass to a group of events is a key principle of the Dempster-Shafer theory. The second limitation is concerned with the observation that in many real-world decision problems we deal with different sources of evidence and the combination of them is needed. The known methods for the rule-base evidential reasoning do not provide a technique for the combination of evidence from different sources. In the current paper, a new approach free of these limitations is proposed. The advantages of this approach are demonstrated using simple numerical examples and the developed stock trading expert system optimized and tested on the real data from Warsaw Stock Exchange.