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Adaptive machine learning approaches utilizing soft decision-making via intuitionistic fuzzy parameterized
Samet Memiş1, Ferhan Şola Erduran2, Hivda Aydoğan3
1Department of Marine Engineering, Faculty of Maritime, Bandırma Onyedi Eylül University, Balıkesir, Türkiye.
Peerj. Computer Science
|March 10, 2025
Summary
Two new adaptive machine learning methods, AIFPIFSC1 and AIFPIFSC2, utilize intuitionistic fuzzy parameterized intuitionistic fuzzy soft matrices for enhanced classification. These approaches demonstrate superior accuracy and robustness on benchmark datasets.
Area of Science:
- Computer Science
- Artificial Intelligence
- Data Science
Background:
- Exponential data growth necessitates advanced analytical methods.
- Machine learning offers adaptable solutions for complex data challenges.
- Existing fuzzy and soft computing methods require enhancement for precision.
Purpose of the Study:
- Introduce two novel adaptive machine learning approaches: AIFPIFSC1 and AIFPIFSC2.
- Enhance machine learning classification using intuitionistic fuzzy parameterized intuitionistic fuzzy soft matrices (ifpifs-matrices).
- Provide a robust framework for soft decision-making in data analysis.
Main Methods:
- Developed AIFPIFSC1 and AIFPIFSC2 utilizing ifpifs-matrices.
- Employed soft decision-making for classification tasks.
- Evaluated performance on 15 University of California, Irvine datasets.
Main Results:
- Proposed methods demonstrated superior performance across six metrics compared to existing fuzzy/soft classifiers.
- Statistical analyses (Friedman, Nemenyi tests) confirmed the reliability and superiority of AIFPIFSC1 and AIFPIFSC2.
- Consistent outperformance highlights the effectiveness for complex classification problems.
Conclusions:
- AIFPIFSC1 and AIFPIFSC2 offer adaptable and effective solutions for modern data analysis.
- The use of ifpifs-matrices significantly enhances machine learning classification.
- This research paves the way for future advancements in machine learning and decision-making systems.
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