Browsing by Author "de Paulis, F."

Now showing 1 - 2 of 2
  • No Thumbnail Available
    ItemMetadata only
    Investigating Confidence Histograms and Classification in FSV: Part I. Fuzzy FSV
    (IEEE, 2013) Di Febo, D.; de Paulis, F.; Orlandi, A.; Zhang, G.; Sasse, Hugh G.; Duffy, A. P.; Wang, L.; Archambeault, B.
    One 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.
  • No Thumbnail Available
    ItemMetadata only
    Investigating Confidence Histograms and Classification in FSV: Part II-Float FSV
    (IEEE, 2013) Di Febo, D.; Archambeault, B.; Zhang, G.; Sasse, Hugh G.; Duffy, A. P.; Wang, L.; de Paulis, F.; Orlandi, A.
    One 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 falls distinctly into one category. The companion paper to this Investigating Confidence histograms and Classification in FSV: Part I. Fuzzy FSV investigated whether allowing probabilistic membership of categories could improve the comparison between FSV and visual assessment. That paper showed that such an approach produced limited improvement and, as a consequence, showed that FSV confidence histograms are robust to flexibility in category boundaries. This paper investigates the effect of redefining some, but not all, category boundaries based around the mode category. This “float” approach does show some improvement in the comparison between FSV and visual assessment.