Investigating Confidence Histograms and Classification in FSV: Part II-Float FSV
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.
Citation : Zhang, G., Sasse, H. and Duffy, A.P. et al (2013) Investigating confidence histograms and classification in FSV: Part II-Float FSV, IEEE Transactions on Electromagnetic Compatibility, 55 (5), pp. 925-932
ISSN : 0018-9375
Research Group : Centre for Electronic and Communications Engineering
Research Institute : Institute of Engineering Sciences (IES)