Nearest-Neighbor Guided Evaluation of Data Reliability and Its Applications

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dc.contributor.author Boongoen, Tossapon
dc.contributor.author Shen, Qiang
dc.date.accessioned 2010-11-29T09:51:53Z
dc.date.available 2010-11-29T09:51:53Z
dc.date.issued 2010-12
dc.identifier.citation Boongoen , T & Shen , Q 2010 , ' Nearest-Neighbor Guided Evaluation of Data Reliability and Its Applications ' IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics , vol 40 , no. 6 , pp. 1622-1633 . , 10.1109/TSMCB.2010.2043357 en
dc.identifier.issn 1083-4419
dc.identifier.other PURE: 154194
dc.identifier.other dspace: 2160/5955
dc.identifier.uri http://hdl.handle.net/2160/5955
dc.identifier.uri http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5454303 en
dc.description T. Boongoen and Q. Shen. Nearest-neighbor guided evaluation of data reliability and its applications. IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, 40(6):1622-1633, 2010. Sponsorship: EPSRC en
dc.description.abstract The intuition of data reliability has recently been incorporated into the main stream of research on ordered weighted averaging (OWA) operators. Instead of relying on human-guided variables, the aggregation behavior is determined in accordance with the underlying characteristics of the data being aggregated. Data-oriented operators such as the dependent OWA (DOWA) utilize centralized data structures to generate reliable weights, however. Despite their simplicity, the approach taken by these operators neglects entirely any local data structure that represents a strong agreement or consensus. To address this issue, the cluster-based OWA (Clus-DOWA) operator has been proposed. It employs a cluster-based reliability measure that is effective to differentiate the accountability of different input arguments. Yet, its actual application is constrained by the high computational requirement. This paper presents a more efficient nearest-neighbor-based reliability assessment for which an expensive clustering process is not required. The proposed measure can be perceived as a stress function, from which the OWA weights and associated decision-support explanations can be generated. To illustrate the potential of this measure, it is applied to both the problem of information aggregation for alias detection and the problem of unsupervised feature selection (in which unreliable features are excluded from an actual learning process). Experimental results demonstrate that these techniques usually outperform their conventional state-of-the-art counterparts. en
dc.format.extent 12 en
dc.language.iso eng
dc.relation.ispartof IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics en
dc.title Nearest-Neighbor Guided Evaluation of Data Reliability and Its Applications en
dc.type Text en
dc.type.publicationtype Article (Journal) en
dc.identifier.doi http://dx.doi.org/10.1109/TSMCB.2010.2043357
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Advanced Reasoning Group en
dc.description.status Peer reviewed en


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