Modeling words by normal interval type-2 fuzzy sets

In this paper, we propose a new method-the HM method-to model words by normal IT2 FSs, using data intervals that are collected from a group of subjects. The HM method uses the same bad data processing, outlier processing and tolerance limit processing to pre-process the data intervals, as is used in the Enhanced Interval Approach (EIA); it then uses a new confidence-interval-based reasonable interval test to keep only those data intervals that share a common interval. In the Fuzzy Set Part, the common overlap is first determined for a group of data intervals; the IT2 FS model for a word is then determined from the remaining data intervals that exclude the overlap. The HM method has a new way to establish if a word should be modeled as a left shoulder, interior or right shoulder IT2 FS. The resulting IT2 FSs have both normal lower and upper membership functions (MFs), which makes them unique among IT2 FS word models. We also compare the IT2 model obtained from the HM method with those from the EIA using Jaccard´s similarity measure.