Comparison of different T-norm operators in classification problems
  Fuzzy rule based classification systems are one of the most popular fuzzy
modeling systems used in pattern classification problems. This paper
investigates the effect of applying nine different T-norms in fuzzy rule based
classification systems. In the recent researches, fuzzy versions of confidence
and support merits from the field of data mining have been widely used for both
rules selecting and weighting in the construction of fuzzy rule based
classification systems. For calculating these merits the product has been
usually used as a T-norm. In this paper different T-norms have been used for
calculating the confidence and support measures. Therefore, the calculations in
rule selection and rule weighting steps (in the process of constructing the
fuzzy rule based classification systems) are modified by employing these
T-norms. Consequently, these changes in calculation results in altering the
overall accuracy of rule based classification systems. Experimental results
obtained on some well-known data sets show that the best performance is
produced by employing the Aczel-Alsina operator in terms of the classification
accuracy, the second best operator is Dubois-Prade and the third best operator
is Dombi. In experiments, we have used 12 data sets with numerical attributes
from the University of California, Irvine machine learning repository (UCI).
