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Computing with words (CWW) has emerged as a powerful tool for processing thelinguistic information, especially the one generated by human beings. VariousCWW approaches have emerged since the inception of CWW, such as perceptualcomputing, extension principle based CWW approach, symbolic method based CWWapproach, and 2-tuple based CWW approach. Furthermore, perceptual computing canuse interval approach (IA), enhanced interval approach (EIA), or Hao-Mendelapproach (HMA), for data processing. There have been numerous works in whichHMA 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 fuzzinessthan the EIA or IA. Thus, we feel that EIA is more suited for word modelling inmulti-person systems and HMA for single-person systems (as EIA is animprovement over IA). Furthermore, another set of works, compared theperformances perceptual computing to the other above said CWW approaches. Inall these works, perceptual computing was shown to be better than other CWWapproaches. However, none of the works tried to investigate the reason behindthis observed better performance of perceptual computing. Also, no comparisonhas been performed for scenarios where the inputs are differentially weighted.Thus, the aim of this work is to empirically establish that EIA is suitable formulti-person systems and HMA for single-person systems. Another dimension ofthis work is also to empirically prove that perceptual computing gives betterperformance than other CWW approaches based on extension principle, symbolicmethod and 2-tuple especially in scenarios where inputs are differentiallyweighted.

An empirical study of computing with words approaches for multi-person and single-person systems