scholarly journals Extracting Wisconsin Breast Cancer Prediction Fuzzy Rules Using Neural Network with Weighted Fuzzy Membership Functions

2004 ◽  
Vol 11B (6) ◽  
pp. 717-722 ◽  
Author(s):  
Joon Shik Lim
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Fuzzy Rules ◽  
Fuzzy Membership ◽  
Membership Functions ◽  
Cancer Prediction ◽  
Fuzzy Membership Functions

Feature selection by a distance measure method of subnormal and non-convex fuzzy sets

2021 ◽  
pp. 1-7
Author(s):  
Letao Qu ◽  
Bohyun Wang ◽  
Joon S. Lim
Keyword(s):  
Feature Selection ◽  
Fuzzy Sets ◽  
Distance Measure ◽  
Critical Role ◽  
Classification Systems ◽  
Fuzzy Rules ◽  
Fuzzy Classification ◽  
Fuzzy Membership ◽  
Membership Functions ◽  
Fuzzy Membership Functions

Distance measures of fuzzy sets have been developed for feature selection and finding redundant features in the fields of decision-making, prediction, and classification problems. Terms commonly used in the definition of fuzzy sets are normal and convex fuzzy sets. This paper extends the general fuzzy set definitions to subnormal and non-convex fuzzy sets that are more precise when implementing uncertain knowledge representations by weighing fuzzy membership functions. A distance measure method for subnormal and non-convex fuzzy sets is proposed for embedded feature selection. Constructing fuzzy membership functions and extracting fuzzy rules play a critical role in fuzzy classification systems. The weighted fuzzy membership functions prevent the combinatorial explosion of fuzzy rules in multiple fuzzy rule-based systems. The proposed method was validated by a comparison with two other methods. Our proposed method demonstrated higher accuracies in training and test, with scores of 97.95% and 93.98%, respectively, compared to the other two methods.

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