Ranking Double fpfs-Matrices: Benchmark of 35 Soft Decision-Making Algorithms

A benchmark evaluation of 35 soft decision-making algorithms based on fuzzy parameterized fuzzy soft matrices (fpfs-matrices). The study uses the FPFS-CMC classifier to rank algorithms across ten datasets from the UCI Machine Learning Repository, assessing performance with metrics like accuracy, precision, recall, specificity, and F1-score. The dataset, created by Ömer Karakoç and last updated in May 2026, provides the comparative results in an XLS file.

Use Cases

• Selecting top-performing soft decision-making algorithms for classification tasks based on the reported F1-score rankings.
• Benchmarking new fuzzy logic-based classification methods against the 35 evaluated algorithms.
• Studying the application of fuzzy parameterized fuzzy soft matrices (fpfs-matrices) for modeling uncertainty in machine learning.
• Designing a decision-support framework for complex machine learning challenges using the structured comparative results.

Strengths

• Benchmarks a specific set of 35 soft decision-making algorithms.
• Evaluates performance across ten diverse UCI Machine Learning Repository datasets.
• Uses multiple standard metrics (accuracy, precision, recall, specificity, F1-score) and statistical significance tests (Friedman, Nemenyi).
• Identifies top-ranked algorithms, including A19 (Rank 1) and YHX14 (Rank 2).

Limitations

• The dataset is very small (5.5 KB), suggesting it contains only summary results, not the underlying raw data.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment for certain analyses.

Provenance

Source

UCI Machine Learning Repository (for the ten evaluation datasets).

Collection Method

Benchmarking performed using the Comparison Matrix-Based Fuzzy Parameterized Fuzzy Soft Classifier (FPFS-CMC).

Freshness

Last updated 2026-05-13 17:44:40; freshness should be verified.