Classification Accuracy of Different Models on Five Multi-View Datasets

A 2026 study by Zhiqi Huang presents classification accuracy results for a novel multi-view TSK fuzzy system (MDA-TSK-FS) and baselines. The dataset likely contains performance metrics from experiments on five public multi-view datasets: Caltech7, Handwritten, Dermatology, Forest, and EEG. The proposed model achieved accuracies of 94.38%, 98.62%, 98.58%, 88.57%, and 69.75% on these datasets, respectively.

Use Cases

• Benchmarking fuzzy system performance based on reported classification accuracy metrics
• Evaluating model adaptability based on the described deformable antecedent structure
• Analyzing rule importance in classification based on the described rule-level attention mechanism
• Comparing model generalization on multi-source heterogeneous data as mentioned in the description

Strengths

• Provides specific classification accuracy numbers (e.g., 94.38% on Caltech7, 69.75% on EEG) for model evaluation
• Includes results from ablation studies quantifying component effectiveness (e.g., 7% and 6% individual improvements on EEG)
• Covers five distinct multi-view datasets, suggesting a multi-domain evaluation scope

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 (5.5 KB), indicating limited scope, likely containing only summary statistics

Provenance

Source

figshare

Collection Method

Results from academic experiments comparing a proposed fuzzy system to baselines.

Freshness

Last updated 2026-05-11 17:22:58