1000 resultados para Information oversampling


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Vehicular networks ensure that the information received from any vehicle is promptly and correctly propagated to nearby vehicles, to prevent accidents. A crucial point is how to trust the information transmitted, when the neighboring vehicles are rapidly changing and moving in and out of range. Current trust management schemes for vehicular networks establish trust by voting on the decision received by several nodes, which might not be required for practical scenarios. It might just be enough to check the validity of incoming information. Due to the ephemeral nature of vehicular networks, reputation schemes for mobile ad hoc networks (MANETs) cannot be applied to vehicular ad hoc networks (VANET). We point out several limitations of trust management schemes for VANET. In particular, we identify the problem of information cascading and oversampling, which commonly arise in social networks. Oversampling is a situation in which a node observing two or more nodes, takes into consideration both their opinions equally without knowing that they might have influenced each other in decision making. We show that simple voting for decision making, leads to oversampling and gives incorrect results. We propose an algorithm to overcome this problem in VANET. This is the first paper which discusses the concept of cascading effect and oversampling effects to ad hoc networks. © 2011 IEEE.

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Learning from imbalanced data is a challenging task in a wide range of applications, which attracts significant research efforts from machine learning and data mining community. As a natural approach to this issue, oversampling balances the training samples through replicating existing samples or synthesizing new samples. In general, synthesization outperforms replication by supplying additional information on the minority class. However, the additional information needs to follow the same normal distribution of the training set, which further constrains the new samples within the predefined range of training set. In this paper, we present the Wiener process oversampling (WPO) technique that brings the physics phenomena into sample synthesization. WPO constructs a robust decision region by expanding the attribute ranges in training set while keeping the same normal distribution. The satisfactory performance of WPO can be achieved with much lower computing complexity. In addition, by integrating WPO with ensemble learning, the WPOBoost algorithm outperformsmany prevalent imbalance learning solutions.

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Developments in information technology will drive the change in records management; however, it should be the health information managers who drive the information management change. The role of health information management will be challenged to use information technology to broker a range of requests for information from a variety of users, including he alth consumers. The purposes of this paper are to conceptualise the role of health information management in the context of a technologically driven and managed health care environment, and to demonstrat e how this framework has been used to review and develop the undergraduate program in health information management at the Queensland University of Technology.

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In some Queensland universities, Information Systems academics have moved out of Business Faculties. This study uses a pilot SWOT analysis to examine the ramifications of Information Systems academics being located within or outside of the Business Faculty. The analysis provides a useful basis for decision makers in the School studied, to exploit opportunities and minimise external threats. For Information Systems academics contemplating administrative relocation of their group, the study also offers useful insights. The study presages a series of further SWOT analyses to provide a range of perspectives on the relative merits of having Information Systems academics administratively located inside versus outside Business faculties.