Showing papers in "Information Sciences in 2008"

TL;DR: In this paper, fuzzy logic is viewed in a nonstandard perspective and the cornerstones of fuzzy logic-and its principal distinguishing features-are: graduation, granulation, precisiation and the concept of a generalized constraint.

TL;DR: A new cooperative coevolution framework that is capable of optimizing large scale nonseparable problems with a random grouping scheme and adaptive weighting and a novel differential evolution algorithm is adopted.

TL;DR: A neighborhood rough set model is introduced to deal with the problem of heterogeneous feature subset selection and Experimental results show that the neighborhood model based method is more flexible to deals with heterogeneous data.

TL;DR: This paper presents a new heuristic similarity measure that focuses on improving recommendation performance under cold-start conditions where only a small number of ratings are available for similarity calculation for each user.

TL;DR: This paper addresses attribute reduction in decision-theoretic rough set models regarding different classification properties, such as decision-monotocity, confidence, coverage, generality and cost, and provides a new insight into the problem of attribute reduction.

TL;DR: The dynamic stochastic selection (DSS) is put forward within the framework of multimember differential evolution, and from the experimental results, DSS-MDE is effective for constrained optimization.

TL;DR: A fuzzy AHP approach is proposed to determine the level of faulty behavior risk (FBR) in work systems and faulty behavior is prevented before occurrence and work system safety is improved.

TL;DR: The intersection, union, ''AND'' operation, and ''OR'' operation of soft ideals and idealistic soft BCK/BCI-algebras are established.

TL;DR: This paper defines the concepts of association matrix and equivalent association matrix, and introduces some methods for calculating the association coefficients of IFSs, and proposes a clustering algorithm for IFS's, which is extended to cluster interval-valued intuitionistic fuzzy sets (IVIFSs).

TL;DR: An efficient centroid type-reduction strategy for general type-2 fuzzy set that usually needs only several resolution of @a value such that the defuzzified value converges to a real value.

TL;DR: This study applies fuzzy linguistic preference relations (Fuzzy LinPreRa) to construct a pairwise comparison matrix with additive reciprocal property and consistency to alleviate inconsistencies in fuzzy AHP method.

TL;DR: A new graphical representation, called Level Diagrams, for n-dimensional Pareto front analysis is proposed, which consists of representing each objective and design parameter on separate diagrams and can be coloured in order to introduce designer preferences.

TL;DR: This work uses triangular fuzzy numbers to express the subjective preferences of evaluators and the fuzzy analytical hierarchy process (AHP) method to compute the relative weights for each criterion, and uses non-additive fuzzy integral to obtain the fuzzy synthetic performance of each common criterion.

TL;DR: A procedure for group decision making based on multiple types of additive linguistic preference relations is presented, and some straightforward formulas to determine the weights of decision makers are given, and a method to reach consensus among the individual preferences and the group's opinion is proposed.

TL;DR: Methods to identify plants by analysing leaf complexity based on estimating their fractal dimension, using a computational program to process, analyze and extract the features of leaf images, thereby allowing for automatic plant identification.

TL;DR: MEPSO has pretty good performance on almost all testing problems adopted in this paper, and outperforms other algorithms when the dynamic environment is unimodal and changes severely, or has a great number of local optima as dynamic Rastrigin function does.

TL;DR: The proposed artificial neural network structure is proposed for seasonal time series forecasting and shows that the proposed model is especially convenient when the seasonality in time series is strong; however, if theSeasonality is weak, different network structures may be more suitable.

TL;DR: The structures of the lower and upper approximations based on arbitrary binary relations in the generalized rough sets are presented and an algorithm to compute atoms for these two Boolean algebras is presented.

TL;DR: The inference capabilities of three different types of fuzzy cognitive maps (FCMs) are compared and guidance is given, in order FCM users to choose the most suitable type of FCM, according to the nature of the problem and the required representation capabilities.

TL;DR: This study demonstrates a sensitivity of more than 90% for the classifier with the specificity of 100% and classification of the four eye diseases was achieved using a three-layer feedforward neural network.

TL;DR: The experimental results on the Graz BCI data set have shown that a LDA classifier, SVM classifier and NN classifier outperform the winner of the BCI 2003 competition on the same data set in terms of either the mutual information, the competition criterion, or misclassification rate.

TL;DR: A new nonprobabilistic entropy of a vague set is proposed by means of the intersection and union of the membership degree and nonmembership degree of the vague set.

TL;DR: Empirical evaluation showed that the proposed algorithm could generate a better discretization scheme that improved the accuracy of classification and the execution time, number of generated rules, and the training time of C5.0.

TL;DR: Based on a new model and an improved separation lemma, the observer-based controller is developed for the asymptotical stabilization of the NCSs, which are shown in terms of nonlinear matrices inequalities.

TL;DR: A vector similarity measure (VSM) is proposed for IT2 FSs, whose two elements measure the similarity in shape and proximity, respectively, which shows that the VSM gives more reasonable results than all other existing similarity measures for IT1 FSs for the linguistic approximation problem.

TL;DR: A linear goal programming model is constructed to integrate the fuzzy assessment information and to directly compute the collective ranking values of alternatives without the need of information transformation to solve the group decision making (GDM) problems with multi-granularity linguistic assessment information.

TL;DR: A replacement strategy for steady-state genetic algorithms that considers two features of the candidate chromosome to be included into the population: a measure of the contribution of diversity to the population and the fitness function.

TL;DR: A novel extended LBP operator for texture analysis that fully uses the texture information contained in the ''nonuniform'' local patterns, which is discarded by the classical LBP operators, and then becomes more robust against noise.

TL;DR: This paper deals with attribute reduction in incomplete information systems and incomplete decision systems based on Dempster-Shafer theory of evidence and shows that in an incomplete information system an attribute set is a belief reduct if and only if it is a classical reduct and a plausibility consistent set must be a classical consistent set.

TL;DR: A general framework for the study of T-fuzzy rough approximation operators in which both the constructive and axiomatic approaches are used, and a notion of fuzziness is introduced.