On the application of the adaptive Power Measure in a Fuzzy Rule-Based Classification System

Inspired by a new sliding window-based adaptive fuzzy measure, this paper presents an application of the Adaptive Power Measure (APM) within Fuzzy Rule-Based Classification Systems (FRBCS) to improve the classification accuracy. The APM is used in the context of the Choquet integral and its generalizations to handle data aggregation in the fuzzy reasoning method of FRBCS. The APM dynamically adjusts the values of the power measure without relying on genetic algorithms (as usually done in the literature), using heuristics to define the exponent q of the standard Power Measure, which is adapted to class-specific characteristics. The methodology includes extensive testing on 33 datasets, employing various heuristic functions for determing q, and examining eight Choquet integral generalizations. Results reveal that the APM frequently matches or outperforms established methods, especially when the effects of the interaction among features are critical. Statistical analyses using the aligned Friedman rank test and Holm’s post-hoc test validate these improvements, establishing the APM as a robust and versatile approach for FRBCS applications.

• Publication year

  2025

• Venue

  IEEE International Conference on Fuzzy Systems

• Publication date

  2025-07-06

• Fields of study

  Mathematics, Computer Science

• Identifiers
  DOI 10.1109/FUZZ62266.2025.11152038
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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