Application of the Sugeno Integral in Fuzzy Rule-Based Classification

Fuzzy Rule-Based Classification System (FRBCS) is a well known technique to deal with classification problems. Recent studies have considered the usage of the Choquet integral and its generalizations to enhance the quality of such systems. Precisely, it was applied to the Fuzzy Reasoning Method (FRM) to aggregate the fired fuzzy rules when classify new data. On the other side, the Sugeno integral, another well known aggregation operator, obtained good results when applied to brain-computer interfaces. Those facts led to the present study in which we consider the Sugeno integral in classification problems. That is, the Sugeno integral is applied in the FRM of a widely used FRBCS and its performance is analyzed over 33 different datasets from the literature. In order to show the efficiency of this new approach, the obtained results are also compared to past studies involving the application of different aggregation functions. Finally, we perform a statistical analysis of the application.

Automated summary

Fuzzy Rule-Based Classification System (FRBCS) is a technique to handle classification problems, and recent studies have explored the use of the Choquet integral and Sugeno integral to improve the system's quality. This study applies the Sugeno integral in the FRM of a widely used FRBCS and analyzes its performance on 33 different datasets. The results are compared to past studies using different aggregation functions, demonstrating the effectiveness of this new approach. A statistical analysis is also conducted.