An empirical study of computing with words approaches for multi-person
  and single-person systems
  Computing with words (CWW) has emerged as a powerful tool for processing the
linguistic information, especially the one generated by human beings. Various
CWW approaches have emerged since the inception of CWW, such as perceptual
computing, extension principle based CWW approach, symbolic method based CWW
approach, and 2-tuple based CWW approach. Furthermore, perceptual computing can
use interval approach (IA), enhanced interval approach (EIA), or Hao-Mendel
approach (HMA), for data processing. There have been numerous works in which
HMA was shown to be better at word modelling than EIA, and EIA better than IA.
But, a deeper study of these works reveals that HMA captures lesser fuzziness
than the EIA or IA. Thus, we feel that EIA is more suited for word modelling in
multi-person systems and HMA for single-person systems (as EIA is an
improvement over IA). Furthermore, another set of works, compared the
performances perceptual computing to the other above said CWW approaches. In
all these works, perceptual computing was shown to be better than other CWW
approaches. However, none of the works tried to investigate the reason behind
this observed better performance of perceptual computing. Also, no comparison
has been performed for scenarios where the inputs are differentially weighted.
Thus, the aim of this work is to empirically establish that EIA is suitable for
multi-person systems and HMA for single-person systems. Another dimension of
this work is also to empirically prove that perceptual computing gives better
performance than other CWW approaches based on extension principle, symbolic
method and 2-tuple especially in scenarios where inputs are differentially
weighted.
