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In this paper, we propose two criteria for efficient sample selection in case of data stream regression problems. The selection becomes apparent whenever the target values, which guide the update of the regressors as well as the implicit model structures, are costly to measure. Reducing the samples used for model updates as much as possible while keeping the predictive accuracy of the models on a...
This paper considers processes with many inputs and outputs from different application areas. Some parts of the inputs are measurable and others are not because of the presence of stochastic environmental factors. This is the reason why processes of this kind operate under uncertainty. As some factors cannot be measured and reflected into the process model, data mining methods cannot be applied. The...
The increasing demand for dealing with uncertainty in data has led to the development of effective and efficient approaches in the data management and mining contexts. Clustering uncertain data objects has particularly attracted great attention in the data mining community. Most existing clustering methods however have urgently to come up with a number of issues, some of which are related to a poor...
The objective of this article is to provide a fair review on certain important aspects related to philosophical foundation of granular computing (GrC). We summarize and analyze existing literature to find out what has led to GrC, what have been explicitly stated, what are implied, and what are still lacking (or overlooked). Due to the huge amount of literature in this area and diverse viewpoints of...
Many applications today need to manage data that is uncertain, such as information extraction (IE), data integration, sensor RFID networks, and scientific experiments. Top-k queries are often natural and useful in analyzing uncertain data in those applications. In this paper, we study the problem of answering top-k queries in a probabilistic framework from a state-of-the-art statistical IE model-semi-Conditional...
The main problems to represent the qualitative concept of "interest" lie in not only fuzziness but also randomness. Cloud model is a model of the uncertain transition between qualitative concept and its quantitative representation. Cloud model can describe randomness and fuzziness in a unified way. This paper analyzes which of the observed user behaviors can be taken as a source of implicit...
Real-life data are frequently imperfect: data may be affected by uncertainty, vagueness, and incompleteness. In this paper, based on dominance relation, the concepts of knowledge granulation and rough entropy of imcomplete information system (include missing data and imprecise data) are defined, their important properties are given, and the relationship between those concepts is established. These...
This paper presents the controller synthesis of a network of local robust controllers for the purpose of controlling the transition from laminar to turbulent flow in non-periodic channels, using a recently proposed spatially interconnected model of plane Poiseuille flow for Re = 2000. Measured feedback signals used by the controllers are the local changes in wall shear force, and the generated control...
The complex network model is the research foundation of complex network, so it is important to evaluate and select model objectively .There is no effective evaluation way up to now. An evaluating method using cloud model is proposed to decrease randomness and fuzziness existing in conventional evaluation. The results of simulating experiments prove that such uncertainty is solved well.
The growing importance of Trust in the realm of open network environment introduces further research on it, due to the special significance of trust to whole system. We view trust as a relation among entities that participate in an action meeting the trustor's own standards for an intended purpose. In this paper, we propose a trust algebra aiming to solving generic trust propagation and trust inference...
In this paper, we propose a method to derive and model data uncertainty from imprecise data. We view data imprecision and errors as the outcome of the precise data exposed to some uncertain channels, and our scheme is to directly derive the data uncertainty model from imprecise data, such that the derived data uncertainty information may be integrated into the succeeding mining process. To achieve...
Selfish behaviors significantly affect the overall performance of mobile ad hoc networks (MANETs). Reputation systems have been proved to be an efficient way to block such behaviors in MANETs. Several reputation models based on subjective logic have been proposed to improve the reputation mechanism, in which an uncertainty value is introduced for reputation computation when the local information is...
The process of modeling concerns itself with the extraction organization of knowledge unambiguously. The rapid advancements in information processing systems are steering engineering research towards the development of intelligent systems. Though the aim of modeling is to provide the human user of the environment to represent knowledge that is convenient to operate, Modeling has become indispensable...
The article deals with a problem of modelling and propagating trust in trust networks (social networks, recommender systems, integrating systems). We briefly recall some of the solutions and approaches proposed by other authors and we focus on a trust model based on IFS theory (Atanassov's intuitionistic fuzzy set theory). Then we present a draft of a method of calculating trust propagation with a...
Coalition formation algorithms are generally not applicable to real-world robotic collectives since they lack mechanisms to handle uncertainty. Those mechanisms that do address uncertainty either deflect it by soliciting information from others or apply reinforcement learning to select an agent type from within a set. This paper presents a coalition formation mechanism that directly addresses uncertainty...
Recently a simple and practical type-2-fuzzistics methodology called an interval approach (IA) was presented for obtaining interval type-2 fuzzy set (IT2 FS) models for words using data collected from a group of subjects. There may be times, however, when a group of subjects is not available. This paper proposes a way to obtain IT2 FS models from words collected from a single subject using an IA,...
Qualitative spatial reasoning is a very important subfield of artificial intelligence and intelligent computation. This paper focuses on qualitative spatial representation and reasoning in 3D space. We firstly describe qualitative 3D space by defining 3D spatial topological and directional relationships between objects. Then we explore spatial reasoning in 3D space by giving the composition tables...
We consider the following fundamental problem: given a matrix that is the sum of an unknown sparse matrix and an unknown low-rank matrix, is it possible to exactly recover the two components? Such a capability enables a considerable number of applications, but the goal is both ill-posed and NP-hard in general. In this paper we develop (a) a new uncertainty principle for matrices, and (b) a simple...
Satisfiability degree extends the satisfiable property of a formula, represents the satisfiable extent of certain properties in model checking, and exhibits it sex actness and convenience for representing real-world uncertainty and fuzziness. Computation of the satisfiability degree of propositional formulas is concerned in this paper. The computation relies on truth table, avoids the obtaining of...
This paper studies the expected value of perfect information (EVPI) and the value of the random fuzzy solution (VRFS) for random fuzzy programming with recourse problems. We first propose three fundamental solution concepts related to the two-stage random fuzzy programming. They are wait-and-see solution, here-and-now solution, and the expected value solution. Then we introduce two important indices...
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