Investigating Confidence Histograms and Classification in FSV: Part I. Fuzzy FSV

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dc.contributor.authorDi Febo, D.en
dc.contributor.authorde Paulis, F.en
dc.contributor.authorOrlandi, A.en
dc.contributor.authorZhang, G.en
dc.contributor.authorSasse, Hugh G.en
dc.contributor.authorDuffy, A. P.en
dc.contributor.authorWang, L.en
dc.contributor.authorArchambeault, B.en
dc.date.accessioned2013-10-15T09:14:40Z
dc.date.available2013-10-15T09:14:40Z
dc.date.issued2013
dc.description.abstractOne important aspect of the feature selective validation (FSV) method is that it classifies comparison data into a number of natural-language categories. This allows comparison data generated by FSV to be compared with equivalent “visual” comparisons obtained using the visual rating scale. Previous research has shown a close relationship between visual assessment and FSV generated data using the resulting confidence histograms. In all cases, the category membership functions are “crisp”: that is data on the FSV value axis fall distinctly into one category. An important open question in FSV-based research, and for validation techniques generally, is whether allowed variability in these crisp category membership functions could further improve agreement with the visual assessment. A similar and related question is how robust is FSV to variation in the categorization algorithm. This paper and its associated “part II” present research aimed at developing a better understanding of the categorization of both visual and FSV data using nonsquare or variable boundary category membership functions. This first paper investigates the level of improvement to be expected by applying fuzzy logic to location of the category boundaries. The result is limited improvement to FSV, showing that FSV categorization is actually robust to variations in category boundaries.en
dc.funderNAen
dc.identifier.citationDi Febo D, de Paulis F, Orlandi A, Zhang G, Sasse H, Duffy A, et al. (2013) Investigating confidence histograms and classification in FSV: Part I. Fuzzy FSV, IEEE Transactions on Electromagnetic Compatibility, 55 (5), pp. 917-924en
dc.identifier.doihttps://doi.org/10.1109/TEMC.2013.2240460
dc.identifier.issn0018-9375
dc.identifier.urihttp://hdl.handle.net/2086/9189
dc.language.isoenen
dc.peerreviewedYesen
dc.projectidNAen
dc.publisherIEEEen
dc.researchgroupCentre for Electronic and Communications Engineeringen
dc.researchinstituteInstitute of Engineering Sciences (IES)en
dc.subjectComputational electromagneticsen
dc.subjectfeature selective validation (FSV)en
dc.subjectmeasurementen
dc.subjectquantitative comparisonen
dc.subjectstatistical methodsen
dc.subjectvalidationen
dc.titleInvestigating Confidence Histograms and Classification in FSV: Part I. Fuzzy FSVen
dc.typeArticleen

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