Showing papers in "Information Sciences in 2007"

TL;DR: The basic concepts of rough set theory are presented and some rough set-based research directions and applications are pointed out, indicating that the rough set approach is fundamentally important in artificial intelligence and cognitive sciences.

TL;DR: Some extensions of the rough set approach are presented and a challenge for the roughSet based research is outlined and it is outlined that the current rough set based research paradigms are unsustainable.

TL;DR: The basic properties of soft sets are introduced, and compare soft sets to the related concepts of fuzzy sets and rough sets, and a definition of soft groups is given.

TL;DR: Methods based on the combination of rough sets and Boolean reasoning with applications in pattern recognition, machine learning, data mining and conflict analysis are discussed.

TL;DR: This paper develops an approach to group decision making based on intuitionistic preference relations and an approach based on incomplete intuitionism preference relations respectively, in which the intuitionistic fuzzy arithmetic averaging operator and intuitionism fuzzy weighted arithmetic averagingoperator are used to aggregate intuitionistic preferences.

TL;DR: A process for quantitative SWOT analysis that can be performed even when there is dependence among strategic factors is demonstrated, which uses the analytic network process (ANP), which allows measurement of the dependency among the strategic factors, as well as AHP, which is based on the independence between the factors.

TL;DR: In this state-of-the-art paper, important advances that have been made during the past five years for both general and interval type-2 fuzzy sets and systems are described.

TL;DR: This paper explores the topological properties of covering-based rough sets, studies the interdependency between the lower and the upper approximation operations, and establishes the conditions under which two coverings generate the same lower approximation operation and the same upper approximation operation.

TL;DR: A new diversity parameter has been used to ensure sufficient diversity amongst the solutions of the non-dominated fronts, while retaining at the same time the convergence to the Pareto-optimal front.

TL;DR: A hybrid approach involving genetic algorithms (GA) and bacterial foraging algorithms for function optimization problems and results clearly illustrate that the proposed approach is very efficient and could easily be extended for other global optimization problems.

TL;DR: This work interprets a fuzzy differential equation by using the strongly generalized differentiability concept, and finds solutions which have a decreasing length of their support and in several applications better reflects the behaviour of some real-world systems.

TL;DR: This paper studies arbitrary binary relation based generalized rough sets, in which a binary relation can generate a lower approximation operation and an upper approximation operation, but some of common properties of classical lower and upper approximation operations are no longer satisfied.

TL;DR: A new SVM approach is proposed, named Enhanced SVM, which combines these two methods in order to provide unsupervised learning and low false alarm capability, similar to that of a supervised S VM approach.

TL;DR: Formulas for computing the cardinality, fuzziness, variance and skewness of an IT2FS are derived and should be useful in IT2 fuzzy logic systems design using the principles of uncertainty, and in measuring the similarity between two IT2 FSs.

TL;DR: This paper proposes that an interval type-2 fuzzy set (IT2 FS) be used as a FS model of a word, because it is characterized by its footprint of uncertainty (FOU), and therefore has the potential to capture word uncertainties.

TL;DR: A lossless and reversible steganography scheme for hiding secret data in each block of quantized discrete cosine transformation (DCT) coefficients in JPEG images that can provide expected acceptable image quality of stego-images and successfully achieve reversibility.

TL;DR: Numerical tests show that the proposed attribute reductions of covering decision systems accomplish better classification performance than those of traditional rough sets.

TL;DR: Three numerical methods to solve ''The fuzzy ordinary differential equations'' are discussed and predictor-corrector is obtained by combining Adams-Bashforth and Adams-Moulton methods.

TL;DR: This work tackles the problem of Project Scheduling Problem by using genetic algorithms (GAs) to solve many different software project scenarios and shows that GAs are quite flexible and accurate for this application, and an important tool for automatic project management.

TL;DR: A fuzzy integrated multi-period and multi-product production and distribution model in supply chain is developed in terms of fuzzy programming and the solution is provided by genetic optimization (genetic algorithm).

TL;DR: This paper deals with the design of control systems using type-2 fuzzy logic for minimizing the effects of uncertainty produced by the instrumentation elements, environmental noise, etc.

TL;DR: An interactive method for multiple attribute group decision making under fuzzy environment that can not only reflect the importance of the given arguments and the ordered positions of the arguments, but also relieve the influence of unfair arguments on the decision result.

TL;DR: This paper discusses the mathematical relationship between intuitionistic fuzzy sets and other models of imprecision.

TL;DR: An adaptive fuzzy control approach is proposed for a class of multiple-input-multiple-output (MIMO) nonlinear systems with completely unknown nonaffine functions by introducing some special type Lyapunov functions and taking advantage of the mean-value theorem, the backstepping design method and the approximation property of the fuzzy systems.

TL;DR: The results show that the combined utilization of SVD with demographic data is promising, since it does not only tackle some of the recorded problems of Recommender Systems, but also assists in increasing the accuracy of systems employing it.

TL;DR: This paper provides new definitions of fuzzy lower and upper approximations by considering the similarity between the two objects and proposes a heuristic algorithm to learn fuzzy rules from initial fuzzy data.

TL;DR: This paper identifies a set of QoS metrics in the context of WS workflows, and proposes a unified probabilistic model for describing QoS values of a broader spectrum of atomic and composite Web services.

TL;DR: The new model for fuzzy rough sets is based on the concepts of both fuzzy covering and binary fuzzy logical operators (fuzzy conjunction and fuzzy implication) and a link between the generalized fuzzy rough approximation operators and fundamental morphological operators is presented in a translation-invariant additive group.

TL;DR: The central idea of the jittered ensemble is adding noises to the input data and thus augments the original training data set to form models based on different but related training samples to consistently outperform the single modeling approach with a variety of time series processes.

TL;DR: The AdROSA system for automatic web banner personalization, which integrates web usage and content mining techniques to reduce user input and to respect users' privacy, is presented in the paper.