Comparison of different T-norm operators in classification problems

by   Fahimeh Farahbod, et al.

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).


page 1

page 2

page 3

page 4


Designing fuzzy rule based classifier using self-organizing feature map for analysis of multispectral satellite images

We propose a novel scheme for designing fuzzy rule based classifier. An ...

CFM-BD: a distributed rule induction algorithm for building Compact Fuzzy Models in Big Data classification problems

Interpretability has always been a major concern for fuzzy rule-based cl...

On Evaluating the Quality of Rule-Based Classification Systems

Two indicators are classically used to evaluate the quality of rule-base...

Scalable Rule-Based Representation Learning for Interpretable Classification

Rule-based models, e.g., decision trees, are widely used in scenarios de...

On the Product Rule for Classification Problems

We discuss theoretical aspects of the product rule for classification pr...

Tracking object's type changes with fuzzy based fusion rule

In this paper the behavior of three combinational rules for temporal/seq...

Exponentially Weighted l_2 Regularization Strategy in Constructing Reinforced Second-order Fuzzy Rule-based Model

In the conventional Takagi-Sugeno-Kang (TSK)-type fuzzy models, constant...