633 research outputs found
Finding Academic Experts on a MultiSensor Approach using Shannon's Entropy
Expert finding is an information retrieval task concerned with the search for
the most knowledgeable people, in some topic, with basis on documents
describing peoples activities. The task involves taking a user query as input
and returning a list of people sorted by their level of expertise regarding the
user query. This paper introduces a novel approach for combining multiple
estimators of expertise based on a multisensor data fusion framework together
with the Dempster-Shafer theory of evidence and Shannon's entropy. More
specifically, we defined three sensors which detect heterogeneous information
derived from the textual contents, from the graph structure of the citation
patterns for the community of experts, and from profile information about the
academic experts. Given the evidences collected, each sensor may define
different candidates as experts and consequently do not agree in a final
ranking decision. To deal with these conflicts, we applied the Dempster-Shafer
theory of evidence combined with Shannon's Entropy formula to fuse this
information and come up with a more accurate and reliable final ranking list.
Experiments made over two datasets of academic publications from the Computer
Science domain attest for the adequacy of the proposed approach over the
traditional state of the art approaches. We also made experiments against
representative supervised state of the art algorithms. Results revealed that
the proposed method achieved a similar performance when compared to these
supervised techniques, confirming the capabilities of the proposed framework
Evaluation of Corporate Sustainability
As a consequence of an increasing demand in sustainable development for business organizations, the evaluation of corporate sustainability has become a topic intensively focused by academic researchers and business practitioners. Several techniques in the context of multiple criteria decision analysis (MCDA) have been suggested to facilitate the evaluation and the analysis of sustainability performance. However, due to the complexity of evaluation, such as a compilation of quantitative and qualitative measures, interrelationships among various sustainability criteria, the assessor’s hesitation in scoring, or incomplete information, simple techniques may not be able to generate reliable results which can reflect the overall sustainability performance of a company. This paper proposes a series of mathematical formulations based upon the evidential reasoning (ER) approach which can be used to aggregate results from qualitative judgments with quantitative measurements under various types of complex and uncertain situations. The evaluation of corporate sustainability through the ER model is demonstrated using actual data generated from three sugar manufacturing companies in Thailand. The proposed model facilitates managers in analysing the performance and identifying improvement plans and goals. It also simplifies decision making related to sustainable development initiatives. The model can be generalized to a wider area of performance assessment, as well as to any cases of multiple criteria analysis
Generalized Evidence Theory
Conflict management is still an open issue in the application of Dempster
Shafer evidence theory. A lot of works have been presented to address this
issue. In this paper, a new theory, called as generalized evidence theory
(GET), is proposed. Compared with existing methods, GET assumes that the
general situation is in open world due to the uncertainty and incomplete
knowledge. The conflicting evidence is handled under the framework of GET. It
is shown that the new theory can explain and deal with the conflicting evidence
in a more reasonable way.Comment: 39 pages, 5 figure
A DEMPSTER-SHAFER MODEL OF RELEVANCE
We present a model for representing relevance and classification decisions of
multiple catalogers in the context of a hierarchical bibliographical database.
The model is based on the Dempster-Shafer theory of evidence. Concepts
like ambiguous relevance, inexact classification, and pooled classification, are
discussed using the nomenclature of belief functions and Dempster's rule.
The model thus gives a normative framework in which one can describe and
address many problematic phenomena which characterize the way people
classify and retrieve documents.Information Systems Working Papers Serie
ON THE USE OF THE DEMPSTER SHAFER MODEL IN INFORMATION INDEXING AND RETRIEVAL APPLICATIONS
The Dempster Shafer theory of evidence concerns the elicitation and manipulation
of degrees of belief rendered by multiple sources of evidence to a common
set of propositions. Information indexing and retrieval applications use a variety
of quantitative means - both probabilistic and quasi-probabilistic - to represent
and manipulate relevance numbers and index vectors. Recently, several
proposals were made to use the Dempster Shafes model as a relevance calculus
in such applications. The paper provides a critical review of these proposals,
pointing at several theoretical caveats and suggesting ways to resolve them.
The methodology is based on expounding a canonical indexing model whose
relevance measures and combination mechanisms are shown to be isomorphic
to Shafer's belief functions and to Dempster's rule, respectively. Hence, the
paper has two objectives: (i) to describe and resolve some caveats in the way
the Dempster Shafer theory is applied to information indexing and retrieval,
and (ii) to provide an intuitive interpretation of the Dempster Shafer theory, as
it unfolds in the simple context of a canonical indexing model.Information Systems Working Papers Serie
Applications of Belief Functions in Business Decisions: A Review
This is the author's final draft. The publisher's official version is available from: .In this paper, we review recent applications of Dempster-Shafer theory (DST) of belief functions
to auditing and business decision-making. We show how DST can better map uncertainties in
the application domains than Bayesian theory of probabilities. We review the applications in
auditing around three practical problems that challenge the effective application of DST,
namely, hierarchical evidence, versatile evidence, and statistical evidence. We review the
applications in other business decisions in two loose categories: judgment under ambiguity and
business model combination. Finally, we show how the theory of linear belief functions, a new
extension of DST, can provide an alternative solution to a wide range of business problems
MULTI-PLAYER BELIEF CALCULI: MODELS AND APPLICATIONS
In developing methods for dealing with uncertainty in reasoning systems, it
is important to consider the needs of the target applications. In particular,
when the source of inferential uncertainty can be tracked to distributions of
expert opinions, there might be different ways to model the representation and
combination of these opinions. In this paper we present the notion of multiplayer
belief calculi - a framework that takes into consideration not only the
'regular' type of evidential uncertainty, but also the diversity of expert opinions
when the evidence is held fixed. Using several applied examples, we show how
the basic framework can be naturally extended to support different application
needs and different sets of assumptions about the nature of the inference process.Information Systems Working Papers Serie
Uncertainty Management of Intelligent Feature Selection in Wireless Sensor Networks
Wireless sensor networks (WSN) are envisioned to revolutionize the paradigm of monitoring complex real-world systems at a very high resolution. However, the deployment of a large number of unattended sensor nodes in hostile environments, frequent changes of environment dynamics, and severe resource constraints pose uncertainties and limit the potential use of WSN in complex real-world applications. Although uncertainty management in Artificial Intelligence (AI) is well developed and well investigated, its implications in wireless sensor environments are inadequately addressed. This dissertation addresses uncertainty management issues of spatio-temporal patterns generated from sensor data. It provides a framework for characterizing spatio-temporal pattern in WSN. Using rough set theory and temporal reasoning a novel formalism has been developed to characterize and quantify the uncertainties in predicting spatio-temporal patterns from sensor data. This research also uncovers the trade-off among the uncertainty measures, which can be used to develop a multi-objective optimization model for real-time decision making in sensor data aggregation and samplin
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