Karakoç Ö., Memiş S., Sennaroğlu B.
PLOS ONE, cilt.21, sa.5, ss.1-38, 2026 (SCI-Expanded, Scopus)
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Yayın Türü:
Makale / Tam Makale
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Cilt numarası:
21
Sayı:
5
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Basım Tarihi:
2026
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Doi Numarası:
10.1371/journal.pone.0348760
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Dergi Adı:
PLOS ONE
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Derginin Tarandığı İndeksler:
Scopus, Science Citation Index Expanded (SCI-EXPANDED), BIOSIS, Chemical Abstracts Core, EMBASE, Index Islamicus, Linguistic Bibliography, MEDLINE, Psycinfo, zbMATH, Directory of Open Access Journals
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Sayfa Sayıları:
ss.1-38
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Marmara Üniversitesi Adresli:
Evet
Özet
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.