Interval MV-algebras and generalizations
For any MV-algebra A we equip the set I(A) of intervals in A with pointwise Lukasiewicz negation @?x={@?@a|@a@?x}, (truncated) Minkowski sum x@?y={@a@?@b|@a@?x,@b@?y}, pointwise Lukasiewicz conjunction x@?y=@?(@?x@?@?y), the operators @Dx=[min@?x,min@?x]...
A survey of fuzzy implication algebras and their axiomatization
The theory of fuzzy implication algebras was proposed by Professor Wangming Wu in 1990. The present paper reviews the following two aspects of studies on FI-algebras: concepts, properties and some subclasses of FI-algebras; axiomatization of the class ...
Computational approaches to finding and measuring inconsistency in arbitrary knowledge bases
There is extensive theoretical work on measures of inconsistency for arbitrary formulae in knowledge bases. Many of these are defined in terms of the set of minimal inconsistent subsets (MISes) of the base. However, few have been implemented or ...
The Goodman-Nguyen relation within imprecise probability theory
The Goodman-Nguyen relation is a partial order generalising the implication (inclusion) relation to conditional events. As such, with precise probabilities it both induces an agreeing probability ordering and is a key tool in a certain common extension ...
Inclusion-exclusion principle for belief functions
The inclusion-exclusion principle is a well-known property in probability theory, and is instrumental in some computational problems such as the evaluation of system reliability or the calculation of the probability of a Boolean formula in diagnosis. ...
Learning continuous time Bayesian network classifiers
Streaming data are relevant to finance, computer science, and engineering while they are becoming increasingly important to medicine and biology. Continuous time Bayesian network classifiers are designed for analyzing multivariate streaming data when ...
Lukasiewicz-based merging possibilistic networks
Possibility theory provides a good framework for dealing with merging problems when information is pervaded with uncertainty and inconsistency. Many merging operators in possibility theory have been proposed. This paper develops a new approach to ...
A rough set-based incremental approach for learning knowledge in dynamic incomplete information systems
With the rapid growth of data sets nowadays, the object sets in an information system may evolve in time when new information arrives. In order to deal with the missing data and incomplete information in real decision problems, this paper presents a ...
Multi-confidence rule acquisition and confidence-preserved attribute reduction in interval-valued decision systems
Rule acquisition is one of the most important objectives in the analysis of decision systems. Because of the interference of errors, a real-world decision system is generally inconsistent, which can lead to the consequence that some rules extracted from ...
A modified rough c-means clustering algorithm based on hybrid imbalanced measure of distance and density
Traditional c-means clustering partitions a group of objects into a number of non-overlapping sets. Rough sets provide more flexible and objective representation than classical sets with hard partition and fuzzy sets with subjective membership function ...
