Showing papers in "Information Sciences in 2011"

TL;DR: A variety of distance measures for hesitant fuzzy sets are proposed, based on which the corresponding similarity measures can be obtained and can alleviate the influence of unduly large deviations on the aggregation results by assigning them low (or high) weights.

TL;DR: The concept of a Z-number has a potential for many applications, especially in the realms of economics, decision analysis, risk assessment, prediction, anticipation and rule-based characterization of imprecise functions and relations.

TL;DR: This work proposes an image cryptosystem employing the Arnold cat map for bit-level permutation and the logistic map for diffusion, demonstrating the superior security and high efficiency of this algorithm.

TL;DR: It is shown that, under certain conditions when considering the costs of different types of miss-classifications, probabilistic three-way decisions are superior to the other two.

TL;DR: A discrete artificial bee colony algorithm is proposed to solve the lot-streaming flow shop scheduling problem with the criterion of total weighted earliness and tardiness penalties under both the idling and no-idling cases.

TL;DR: This paper makes a comparative study of the effectiveness of ensemble technique for sentiment classification, with the aim of efficiently integrating different feature sets and classification algorithms to synthesize a more accurate classification procedure.

TL;DR: It is shown that Pawlak's rough set model can be viewed as a special case of the soft rough sets, and these two notions will coincide provided that the underlying soft set in the soft approximation space is a partition soft set.

TL;DR: An overview of the proposed interpretability measures and techniques for obtaining more interpretable linguistic fuzzy rule-based systems is presented and a taxonomy based on a double axis is proposed: ''Complexity versus semantic interpretability'' considering the two main kinds of measures.

TL;DR: Methods for measuring the entropy of graphs are described and relationships between selected entropy measures are examined, illustrating differences quantitatively with concrete examples.

TL;DR: An enhanced PSO algorithm called GOPSO is presented, which employs generalized opposition-based learning (GOBL) and Cauchy mutation to overcome the problem of premature convergence when solving complex problems.

TL;DR: An extensive survey of protocols developed according to the principles of swarm intelligence, taking inspiration from the foraging behaviors of ant and bee colonies, and introduces a novel taxonomy for routing protocols in wireless sensor networks.

TL;DR: The notion of bipolar fuzzy graphs is introduced, various methods of their construction are described, the concept of isomorphisms of these graphs are discussed, and some of their important properties are investigated.

TL;DR: This study facilitates the improvement of collaborator relationships and the management of potential suppliers to help increase product development capability and quality, reduce product lifecycle time and cost, and thus increase product marketability.

TL;DR: Numerical results show that the new algorithm is promising in terms of convergence speed, success rate, and accuracy, and the proposed RABC is also capable of keeping up with the direction changes in the problems.

TL;DR: A self-adaptive learning based PSO (SLPSO) is proposed to make up the above demerits, which can update the best solution records on 26 numerical optimization problems with different characteristics such as uni-modality, multi- modality, rotation, ill-condition, mis-scale and noise.

TL;DR: A context-sensitive technique for unsupervised change detection in multitemporal remote sensing images based on fuzzy clustering approach and takes care of spatial correlation between neighboring pixels of the difference image produced by comparing two images acquired on the same geographical area at different times.

TL;DR: An entropy measure for interval-valued intuitionistic fuzzy sets is proposed, which generalizes three entropy measures defined independently by Szmidt, Wang and Huang, for intuitionism fuzzy sets and is applied to solve problems on pattern recognitions, multi-criteria fuzzy decision making and medical diagnosis.

TL;DR: A new construction method based on the Lukasiewicz triangular norm is proposed, which is consistent with operations on ordinary fuzzy sets, and therefore is a true generalization of such operations.

TL;DR: This hybrid algorithm, called maximum relevance minimum redundancy PSO (mr^2PSO), is novel in the sense that it uses the mutual information available from the filter model to weigh the bit selection probabilities in the discrete PSO.

TL;DR: This paper points out that when tests must be undertaken in parallel, attribute reduction is mandatory in dealing with the minimal test cost reduct problem, and proposes a framework for a heuristic algorithm to deal with the new problem.

TL;DR: A discrete artificial bee colony algorithm hybridized with a variant of iterated greedy algorithms to find the permutation that gives the smallest total flowtime is presented.

TL;DR: This paper proposes and compares two FCA-based methods for mining numerical data and shows that they are equivalent and the first one relies on a particular scaling, encoding all possible intervals of attribute values, and uses standard FCA techniques.

TL;DR: An embedded method that simultaneously selects relevant features during classifier construction by penalizing each feature's use in the dual formulation of support vector machines (SVM) called kernel-penalized SVM (KP-SVM).

TL;DR: This two-part state-of-the-art overview on aggregation theory summarizes the essential information concerning aggregation issues and gives an overview of aggregation properties, including the basic classification on aggregation functions.

TL;DR: This article provides a comprehensive and comparative overview of question answering technology and suggests a general question answering architecture that steadily increases the complexity of the representation level of questions and information objects.

TL;DR: It is revealed that the proposed framework and procedures can enhance the agility of an enterprise as well as ensure a competitive edge.

TL;DR: This work presents a general rule induction algorithm based on sequential covering, suitable for variable consistency rough set approaches, and shows how to improve rule induction efficiency due to application of consistency measures with desirable monotonicity properties.

TL;DR: The proposed MSGP-based solutions are capable of effectively simulating the nonlinear behavior of the investigated systems and are found to be more accurate than those of standard GP and artificial neural network-based models.

TL;DR: A formal approach to granular computing with multi-scale data measured at different levels of granulations is proposed in this paper and the unravelling of decision rules at different scales in multi- scale decision tables is discussed.

TL;DR: An effective memetic differential evolution (DE) algorithm that utilizes a chaotic local search (CLS) with a 'shrinking' strategy that is significantly better than, or at least comparable to, the other optimizers in terms of convergence performance and solution accuracy.