Computational Complexity of 35 Soft Decision-Making Algorithms via Fuzzy Matrices

A benchmark of 35 soft decision-making algorithms based on fuzzy parameterized fuzzy soft matrices. The evaluation used the FPFS-CMC classifier across ten UCI datasets, assessing performance with accuracy, precision, recall, specificity, and F1-score. The dataset, authored by Ömer Karakoç and shared under CC-BY-4.0, was last updated on May 13, 2026.

Use Cases

• Benchmarking algorithm performance based on metrics like accuracy, precision, recall, specificity, and F1-score.
• Selecting effective soft decision-making methods for classification tasks involving uncertainty.
• Comparing the computational complexity of different SDM algorithms via fuzzy matrices.
• Validating algorithm rankings using statistical tests like Friedman and Nemenyi.

Strengths

• Benchmarks 35 distinct soft decision-making algorithms.
• Evaluates performance across ten diverse datasets from the UCI Machine Learning Repository.
• Uses multiple established metrics (accuracy, precision, recall, specificity, F1-score) and statistical significance tests.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The dataset is very small at 5.5 KB, indicating limited scope.

Provenance

Source

figshare

Collection Method

Benchmarking study using the FPFS-CMC classifier on UCI datasets.

Freshness

Last updated 2026-05-13 17:44:43; freshness should be verified.