Showing papers in "Information Sciences in 2009"

TL;DR: A new optimization algorithm based on the law of gravity and mass interactions is introduced and the obtained results confirm the high performance of the proposed method in solving various nonlinear functions.

TL;DR: This paper proposes a method called Mlnb which adapts the traditional naive Bayes classifiers to deal with multi-label instances and achieves comparable performance to other well-established multi- label learning algorithms.

TL;DR: The equivalency between this type of covering-based rough sets and a type of binary relation based rough sets is established and axiomatic systems for this type-of-covering lower and upper approximation operations are presented.

TL;DR: A novel wrapper Algorithm for Feature Selection, using Support Vector Machines with kernel functions, based on a sequential backward selection, using the number of errors in a validation subset as the measure to decide which feature to remove in each iteration.

TL;DR: The induced generalized ordered weighted averaging (IGOWA) operator is a new aggregation operator that generalizes the OWA operator, including the main characteristics of both the generalized OWA and the induced OWA operators.

TL;DR: A new ranking method and a new similarity measure for IT2 FSs are proposed and the results are useful in understanding the uncertainties associated with linguistic terms and hence how to use them effectively in survey design and linguistic information processing.

TL;DR: A tracking controller for the dynamic model of a unicycle mobile robot is described by integrating a kinematic and a torque controller based on type-2 fuzzy logic theory and genetic algorithms.

TL;DR: An approach to multiattribute decision making with incomplete attribute weight information where individual assessments are provided as interval-valued intuitionistic fuzzy numbers (IVIFNs) is proposed by employing a series of optimization models.

TL;DR: This paper presents a systematic approach for decreasing conservativeness in stability analysis and control design for Takagi-Sugeno (TS) systems based on the idea of multiple Lyapunov functions together with simple techniques for introducing slack matrices.

TL;DR: According to this study, Random Forests provides the best prediction performance for large datasets and Naive Bayes is thebest prediction algorithm for small datasets in terms of the Area Under Receiver Operating Characteristics Curve (AUC) evaluation parameter.

TL;DR: Three interval type-2 fuzzy neural network (IT2FNN) architectures are proposed, with hybrid learning algorithm techniques (gradient descent backpropagation and gradient descent with adaptive learning rate back Propagation) and proved to be more efficient mechanism for modeling real-world problems.

TL;DR: The equivalence of the unary covering and the covering with the property that the intersection of any two elements is the union of finite elements in this covering is established.

TL;DR: This paper proposes a reduct construction method based on discernibility matrix simplification, which works in a similar way to the classical Gaussian elimination method for solving a system of linear equations.

TL;DR: The relation between strong paths and strongest paths in a fuzzy graph is analyzed and characterizations for fuzzy bridges, fuzzy trees and fuzzy cycles are obtained using the concept of @a-strong, @b-strong and @d-arcs.

TL;DR: This study proposes an efficient service selection scheme to help service requesters select services by considering two different contexts: single QoS-based service discovery and QoS -based optimization of service composition.

TL;DR: A forecasting framework based on the fuzzy multi-criteria decision making (FMCDM) approach is developed to help organizations build awareness of the critical influential factors on the success of knowledge management (KM) implementation, measure the success possibility ofknowledge management projects, as well as identify the necessary actions prior to embarking on conducting knowledge management.

TL;DR: Various agent-based models relevant to host–pathogen systems and their contributions to the authors' understanding of biological processes are reviewed and some limitations and challenges are pointed out.

TL;DR: The collapsing method converts an interval type-2 fuzzy set into a type-1 representative embedded set (RES), whose defuzzified values closely approximates that of the type- 2 set.

TL;DR: This paper develops a general model with learning effects where the actual processing time of a job is not only a function of the total normal processing times of the jobs already processed, but also afunction of the job's scheduled position.

TL;DR: This paper investigates three different classification properties, and suggests three distinct definitions accordingly, based on the common structure of the specific definitions of relative reducts and discernibility matrices.

TL;DR: A method to classify blogs based on their information content is presented, which exploits high-level features describing the medical and affective content of blog posts, and shows that there are substantial differences in the content of various health-related Web resources.

TL;DR: In this paper, three novel interval type-2 fuzzy membership function (IT2 FMF) generation methods are proposed, based on heuristics, histograms, and interval type -2 fuzzy C-means, which are evaluated by applying them to back-propagation neural networks (BPNNs).

TL;DR: The output-feedback control problem is considered for networked systems involving in signal quantization and data packet dropout and an estimation method is introduced to cope with the effect of random packet loss that is modelled as a Bernoulli process.

TL;DR: A new dominant selection operator that enhances the action of the dominant individuals, along with a cyclical mutation operator that periodically varies the mutation probability in accordance with evolution generation found in biological evolutionary processes are introduced.

TL;DR: A new optimality criterion based on preference order (PO) scheme is used to identify the best compromise in multi-objective particle swarm optimization (MOPSO).

TL;DR: This work combines the fuzzy analytic hierarchy process (AHP) with the portfolio selection problem and shows that both of the models provide both ranking and weighting information, via fuzzy AHP, to the investors in this financial scenario.

TL;DR: This work introduces the conjunctive/disjunctive set-valued ordered information systems, and develops an approach to queuing problems for objects in presence of multiple attributes and criteria.

TL;DR: An axiomatic definition of knowledge granulation for an information system is given, under which these three measures are modified and show that the modified measures are effective and suitable for evaluating the roughness and accuracy of a set in an Information system and the approximation accuracy of an rough classification in a decision table.

TL;DR: This paper proposed a novel approach to ranking fuzzy numbers based on the left and right deviation degree (L-R deviation degree) of fuzzy number, and the ranking index value is obtainedbased on the L-R deviations degree and relative variation of fuzzy numbers.

TL;DR: This work proposes a novel hybrid recommendation method that combines the segmentation-based sequential rule method with the segmentations-based KNN-CF method, and shows that the hybrid method outperforms traditional CF methods.