An Adaptive Fuzzy Min-Max Neural Network Classifier Based on Principle Component Analysis and Adaptive Genetic Algorithm : Table 7

Table 7: Comparison with other traditional classifiers.


Technique	Misclassification

Bayes classifier¹	2
k-nearest neighborhood¹	4
Fuzzy k-nn²	4
Fisher ratios¹	3
Ho-kashyap¹	2
Perceptron³	3
Fuzzy perceptron³	2
FMNN¹	2
GFMN¹	1/0
GFMN³	0
FMCN¹	0
FMCN³	0
AFMN¹	0
AFMN³	0

Training set is of 75 data points (25 from each class) and test set consists of remaining data points.
²Training data is of 36 data points (12 from each class) and test set consists of 36 data points; results are then scaled up for 150 points.
³Training and testing data are the same.