On the Efficiency of the Neuro-Fuzzy Classifier for User Knowledge Modeling Systems

TL;DR

This paper evaluates the efficiency of neuro-fuzzy classifiers, specifically MLP and ANFIS, for user knowledge modeling, demonstrating superior performance over traditional methods with high accuracy on real data.

Contribution

The study introduces and compares neuro-fuzzy classifiers for user knowledge modeling, highlighting their effectiveness over conventional classifiers like KNN and Bayesian methods.

Findings

Neuro-fuzzy classifiers achieved 98.6% test accuracy.

ANFIS outperformed MLP in accuracy, but MLP was more efficient overall.

Multiple evaluation metrics confirmed the robustness of neuro-fuzzy methods.

Abstract

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.