rice: Rice taste data
In rrcov: Scalable Robust Estimators with High Breakdown Point
rice R Documentation
Rice taste data
Description
The rice taste data consists of five inputs and a single output whose values
are associated with subjective evaluations as follows: xl: flavor,
x2: appearance, x3: taste, x4: stickiness, x5: toughness, y: overall evaluation.
Sensory test data have been obtained by such subjective evaluations for 105 kinds of
rice (e.g., Sasanishiki, Akita-Komachi, etc.). The data set was used by
Nozaki et al. (1997) to demonstrate the high performance of a proposed
for automatically generating fuzzy if-then rules from numerical data.
Usage
data(rice)
Format
A data frame with 105 observations on the following 6 variables:
Favorcompactness
Appearancecircularity
Tastedistance circularity
Stickinessradius ratio
Toughnessprincipal axis aspect ratio
Overall_evaluationmaximum length aspect ratio
Source
Nozaki, K., Ishibuchi, H, and Tanaka, H. (1997)
A simple but powerful heuristic method for generating fuzzy rules from numerical data
Fuzzy Sets and Systems 86 3 p. 251–270.
rrcov documentation built on May 29, 2024, 1:13 a.m.
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| rice | R Documentation |
Rice taste data
Description
The rice taste data consists of five inputs and a single output whose values are associated with subjective evaluations as follows: xl: flavor, x2: appearance, x3: taste, x4: stickiness, x5: toughness, y: overall evaluation. Sensory test data have been obtained by such subjective evaluations for 105 kinds of rice (e.g., Sasanishiki, Akita-Komachi, etc.). The data set was used by Nozaki et al. (1997) to demonstrate the high performance of a proposed for automatically generating fuzzy if-then rules from numerical data.
Usage
data(rice)Format
A data frame with 105 observations on the following 6 variables:
Favorcompactness
Appearancecircularity
Tastedistance circularity
Stickinessradius ratio
Toughnessprincipal axis aspect ratio
Overall_evaluationmaximum length aspect ratio
Source
Nozaki, K., Ishibuchi, H, and Tanaka, H. (1997) A simple but powerful heuristic method for generating fuzzy rules from numerical data Fuzzy Sets and Systems 86 3 p. 251–270.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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