scholarly journals On a Novel Way of Processing Data that Uses Fuzzy Sets for Later Use in Rule-Based Regression and Pattern Classification

Author(s):  
Jerry M. Mendel
Author(s):  
Tomoharu Nakashima ◽  
◽  
Yasuyuki Yokota ◽  
Hisao Ishibuchi ◽  
Gerald Schaefer ◽  
...  

We evaluate the performance of cost-sensitive fuzzy-rule-based systems for pattern classification problems. We assume that a misclassification cost is given a priori for each training pattern. The task of classification thus becomes to minimize both classification error and misclassification cost. We examine the performance of two types of fuzzy classification based on fuzzy if-then rules generated from training patterns. The difference is whether or not they consider misclassification costs in rule generation. In our computational experiments, we use several specifications of misclassification cost to evaluate the performance of the two classifiers. Experimental results show that both classification error and misclassification cost are reduced by considering the misclassification cost in fuzzy rule generation.


Author(s):  
R Rahmawati

Current problems often do not have certain answers. The use of fuzzy mamdani logic to determine a level of achievement of the success of the teacher in teaching students at MI Mambaul Ulum Al Amin Sampit is the content of this paper. The problem will be solved by determining the level of achievement of the teacher's success in teaching students, if only two input variables are used, namely the teacher and also the value. So the first thing to solve the problem of the level of achievement of teacher success in teaching through the fuzzy mamdani method is to determine the input and output variables of a firm set. The second thing is changing the input variables into fuzzy sets through fuzzyfication. The third thing is processing data from fuzzy sets using the maximum system. The last or fourth thing is changing the results issued into a firm set through defuzzyfication with the centroid method, so the results are as desired in the output variable. The calculation of fuzzy mamdani results in an achievement level of success for MI teachers with a teacher variable value of 55 and a variable value of 80


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