Showing papers in "International Journal of Approximate Reasoning in 2011"

TL;DR: The relationship between intutionistic fuzzy set and hesitant fuzzy set is discussed, based on which some operations and aggregation operators for hesitant fuzzy elements are developed and their application in solving decision making problems is given.

TL;DR: The current contribution constitutes a review on the most representative genetic fuzzy systems relying on Mamdani-type fuzzy rule-based systems to obtain interpretable linguistic fuzzy models with a good accuracy.

TL;DR: This work identifies the syntactic differences that a fuzzy ontology language has to cope with, and proposes a concrete methodology to represent fuzzy ontologies using OWL 2 annotation properties.

TL;DR: A logarithmic fuzzy preference programming (LFPP) based methodology for fuzzy AHP priority derivation is proposed, which formulates the priorities of a fuzzy pairwise comparison matrix as a logarithsmic nonlinear programming and derives crisp priorities from fuzzy Pairwise comparison matrices.

TL;DR: This paper presents flexible algorithms for learning MBC structures from data based on filter, wrapper and hybrid approaches, and derives theoretical results on how to minimize the expected loss under standard 0-1 loss functions.

TL;DR: This paper provides a complete development of the AFRA formalism complemented by illustrative examples and a detailed comparison with other recursive attack formalizations.

TL;DR: A method for conflict management within Dempster-Shafer theory is developed that each piece of evidence is discounted in proportion to the degree that it contributes to the conflict.

TL;DR: The results show empirically that the use of the methodology outperforms the initial Fuzzy Rule-Based Classification System and also improves the behavior of the genetic tuning based on the 3-tuples fuzzy linguistic representation.

TL;DR: A new feature selection algorithm is presented based on rough set theory that selects a set of genes from microarray data by maximizing the relevance and significance of the selected genes.

TL;DR: The main goal of this paper is to describe inference in hybrid Bayesian networks (BNs) using mixture of polynomials (MOP) approximations of probability density functions (PDFs), which are similar in spirit to using mixtures of truncated exponentials (MTEs) approxims.

TL;DR: In this paper, the authors further investigate the covering rough sets based on neighborhoods by approximation operations and show that the upper approximation based on neighborhood can be defined equivalently without using neighborhoods.

TL;DR: It is proved that two known topologies corresponding to reflexive relation based rough set model given recently are different, and gave a condition under which the both are the same topology.

TL;DR: Overall, optimizing a single t-norm based rule yields better results than using a fixed rule, including Dempster's rule, and the two-step strategy brings further improvements.

TL;DR: A counting sort algorithm is introduced with time complexity O(|C| |U|) for dealing with redundant and inconsistent data in a decision table and computing positive regions and core attributes, and hybrid attribute measures are constructed which reflect the significance of an attribute inpositive regions and boundary regions.

TL;DR: An overview of the computational complexity of many problem variants, including enumeration variants, parameterized problems, and approximation strategies to the MPE-problem with and without additional (neither observed nor explained) variables is given.

TL;DR: This paper identifies a set of properties -- which it is called “logical view” -- that is expected to hold in the implicit semantics and is used in the approach to evaluate the cointension between explicit and implicit semantics.

TL;DR: An overview of some of the fundamental concepts for reasoning with uncertainty expressed in terms of desirable gambles in the finite case are given, a characterization of regular extension is provided, and the nature of maximally coherent sets of desirable gamblers is studied.

TL;DR: It is proved that a complex massive covering information system can be compressed into a relatively small-scale information system and its attribute reduction is invariant under the condition of homomorphism.

TL;DR: This paper proposes an algorithm for generating decision rules that take into account local patterns for arriving at a particular decision in a sequential information system, and uses the discernibility of decision classes to establish evaluation criteria for the decision rules in the sequential Information system.

TL;DR: A systematic method which bridges the fuzzy set methodology and probabilistic approach of RS to solve the threshold value @b determination problem in variable precision rough sets (VPRS) is proposed.

TL;DR: This paper formalises and extends the theory of p-boxes using Walley's behavioural theory of imprecise probabilities, and heavily rely on its notion of natural extension and existing results about independence modeling, and allows p- boxes to be defined on arbitrary totally preordered spaces, thereby also admitting multivariate p-box via probability bounds over any collection of nested sets.

TL;DR: It is shown how to represent clustering results as masses of evidence allocated to sets of partitions, and a consensus belief function is obtained using a suitable combination rule.

TL;DR: A normalized framework for measuring the degree of inconsistency of a belief base which unifies the impact of both consistent subsets and minimal inconsistent subsets is proposed and satisfies all the properties deemed necessary by common consent to characterize an intuitively satisfactory measure.

TL;DR: It is proved that the action of automorphisms and the canonical construction of De Morgan triples on L∗ commutes.

TL;DR: In this paper, the problem of attribute reduction for partially labeled data is first studied, and a novel rough co-training model is proposed, which outperforms other representative models in terms of accuracy and even compares favorably with rough classifier trained on all training data labeled.

TL;DR: The computation of the family of measures presented here, in as much as it yields an adjustment in the probability of each statement that restores consistency, provides the modeler with possible repairs of the knowledge base.

TL;DR: A dominance-based fuzzy rough set approach for the decision analysis of a preference-ordered uncertain or possibilistic data table, which is comprised of a finiteSet of objects described by a finite set of criteria.

TL;DR: Two combination rules are introduced based on cautious combinations of plausibility and commonality functions based on Dempster's rules of conditioning and combination and the minimum rule of possibility theory for belief functions obtained from not necessarily independent sources of information.

TL;DR: This paper first defines epistemic states to represent beliefs attached with strength, and presents a set of postulates to describe the change process on epistemicStates that is determined by the strengths of input, and establishes representation theorems to characterize these postulates.

TL;DR: A new definition of integral-like functionals exploiting the ideas of the Lebesgue integral construction and extending the idea of pan-integrals is given and several types of integrals known from the literature are shown to be special cases of generalized Lebesque integral.