MetaFS

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💡 Overview

Choosing the right feature selection algorithm for a given dataset is a non-trivial task that typically requires extensive experimentation. MetaFS addresses this by framing algorithm selection as a meta-learning problem: given a new dataset, a set of meta-features is extracted and used to predict the performance of eight feature selection algorithms across five evaluation criteria. MetaFS is available as a web application on Hugging Face Spaces. Upload your dataset, select the target column, adjust the composite criterion weights, and get algorithm rankings in seconds.

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🧠 Methodology

Meta-learners are Ridge regression models trained on a metadataset of 40 benchmark classification datasets under a Leave-One-Dataset-Out (LODO) evaluation protocol. Predictions are generated instantly at inference time, providing ranked recommendations without running any feature selection method on the target dataset.

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🔬 Algorithms

The following feature selection methods are evaluated and ranked by MetaFS:

Algorithm	Package	Reference
ANOVA F-value	scikit-learn	SelectKBest(f_classif)
Boruta	boruta	BorutaPy
CCEA	pyccea	CCEA
Chi-Square	scikit-learn	SelectKBest(chi2)
Genetic Algorithm	deap	algorithms.eaSimple
MRMR	mrmr-selection	mrmr_classif
Mutual Information	scikit-learn	SelectKBest(mutual_info_classif)
PCA	scikit-learn	PCA

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📊 Evaluation Criteria

Criterion	Direction	Description
Balanced Accuracy	↑ Higher is better	Average per-class accuracy after feature selection, robust to class imbalance.
F1 Score	↑ Higher is better	Harmonic mean of precision and recall after feature selection.
Compression Ratio	↑ Higher is better	Fraction of features removed; 1 means all features discarded.
Feature Selection Time	↓ Lower is better	Wall-clock time (seconds) to run the feature selection algorithm.
Composite	↑ Higher is better	User-weighted average of Balanced Accuracy and Compression Ratio.

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⚙️ Meta-features

MetaFS extracts the following 10 dataset meta-features to characterize each new dataset:

Meta-feature	Description
ImbalanceRatio	Ratio between the largest and smallest class counts.
MaxFeatureClassSpearman	Maximum absolute Spearman correlation between any feature and the target.
MF_Dimensionality	Ratio of features to samples.
MF_MaxNumericMutualInformation	Maximum mutual information between any feature and the target.
MF_MaxCardinalityOfNumericFeatures	Maximum number of unique values across all numeric features.
MF_StdevNumericMutualInformation	Standard deviation of mutual information scores across features.
MF_Quartile1ClassProbability	First quartile of the class probability distribution.
MF_MinClassProbability	Minimum class probability.
MF_MaxNumericJointEntropy	Maximum joint entropy between any feature and the target.
MF_KurtosisClassProbability	Kurtosis of the class probability distribution (Fisher's excess).

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📜 Citation

If you use MetaFS in your research, please cite:

@misc{MetaFS,
    author = {Venâncio, Pedro},
    title = {{MetaFS}: A meta-learning approach to feature selection algorithm recommendation for binary classification tasks},
    year = {2026},
    publisher = {GitHub},
    url = {https://github.com/pedbrgs/MetaFS}
}

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📫 Contact

Please send any bug reports, questions or suggestions directly in the repository.