Classification of soft decision-making methods via fuzzy parameterized fuzzy soft matrices and their performance-based statistical analysis in machine learning
Clinical Snapshot
PICO Framework
| P — Population | 35 soft decision-making algorithms using fuzzy parameterized fuzzy soft matrices |
| I — Intervention | Comparison Matrix-Based Fuzzy Parameterized Fuzzy Soft Classifier (FPFS-CMC) benchmarking |
| C — Comparator | Comparative analysis across algorithms using statistical testing |
| O — Outcomes | Classification performance metrics (accuracy, precision, recall, specificity, F1-score) |
Bottom Line
This methodological study benchmarked 35 soft decision-making algorithms using fuzzy parameterized fuzzy soft matrices across standard machine learning datasets. The research identified A19 as the top-performing algorithm based on F1-scores, followed by YHX14. While the study provides valuable comparative data for algorithm selection in uncertainty-based classification tasks, clinical applicability requires further validation in healthcare-specific datasets. The statistical rigor using Friedman and Nemenyi tests strengthens the findings, though confidence intervals and computational cost considerations are lacking. For Australian healthcare applications, these algorithms could support clinical decision support systems, particularly where uncertainty quantification is crucial, but implementation would require careful validation against local clinical datasets and compliance with TGA software regulations.
Key Findings
P Value: Statistical significance confirmed via Friedman and Nemenyi tests (specific values not provided)
Effect Size: A19 ranked first, YHX14 second, three-way tie for third (VMH16, AKO18o, A19/2)
Primary Outcome: Algorithm ranking based on F1-score performance
Nnt Or Sensitivity: F1-score used as primary performance metric combining precision and recall
Confidence Interval: Not reported
Clinical Application
Requires computational expertise and appropriate software implementation Relevant for Australian digital health initiatives and AI implementation in healthcare, aligns with TGA software as medical device guidelines Healthcare machine learning applications requiring uncertainty modeling and soft decision-making
Abstract
This study provides a comprehensive evaluation and classification of 35 soft decision-making (SDM) algorithms based on fuzzy parameterized fuzzy soft matrices (fpfs-matrices). Although fpfs-matrices offer a strong mathematical framework for modeling uncertainty, there has been a lack of large-scale comparisons of their derivative SDM methods in machine learning. To address this, we used the Comparison Matrix-Based Fuzzy Parameterized Fuzzy Soft Classifier (FPFS-CMC) to benchmark these 35 algorithms across ten diverse datasets from the UCI Machine Learning Repository. The methods were thoroughly assessed utilizing metrics such as accuracy, precision, recall (sensitivity), specificity, and F1-score, and statistical significance was confirmed using the Friedman and Nemenyi tests. Our results show that SDM methods via fpfs-matrices perform competitively in classification tasks involving uncertainty. Notably, the best algorithms according to F1-scores were A19 (Rank 1), YHX14 (Rank 2), and a three-way tie for Rank 3 among VMH16, AKO18o, and A19/2. By identifying the most effective algorithms and offering a structured decision-support framework, this research provides both a theoretical reference and practical guidance for practitioners selecting SDM methods for complex machine learning challenges.
References
- 1.Karakoç, Ö., Memiş, S., & Sennaroglu, B. (2026). Classification of soft decision-making methods via fuzzy parameterized fuzzy soft matrices and their performance-based statistical analysis in machine learning. PLoS One. https://doi.org/10.1371/journal.pone.0348760
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