On the Efficiency of the Neuro-Fuzzy Classifier for User Knowledge
  Modeling Systems
  User knowledge modeling systems are used as the most effective technology for
grabbing new user's attention. Moreover, the quality of service (QOS) is
increased by these intelligent services. This paper proposes two user knowledge
classifiers based on artificial neural networks used as one of the influential
parts of knowledge modeling systems. We employed multi-layer perceptron (MLP)
and adaptive neural fuzzy inference system (ANFIS) as the classifiers.
Moreover, we used real data contains the user's degree of study time,
repetition number, their performance in exam, as well as the learning
percentage, as our classifier's inputs. Compared with well-known methods like
KNN and Bayesian classifiers used in other research with the same data sets,
our experiments present better performance. Although, the number of samples in
the train set is not large enough, the performance of the neuro-fuzzy
classifier in the test set is 98.6% which is the best result in comparison with
others. However, the comparison of MLP toward the ANFIS results presents
performance reduction, although the MLP performance is more efficient than
other methods like Bayesian and KNN. As our goal is evaluating and reporting
the efficiency of a neuro-fuzzy classifier for user knowledge modeling systems,
we utilized many different evaluation metrics such as Receiver Operating
Characteristic and the Area Under its Curve, Total Accuracy, and Kappa
statistics.
