A-GRASPQ-FS

Adaptive GRASPQ Feature Selection with self-tuning subset size

A-GRASPQ-FS is a scikit-learn compatible feature selector based on a reactive GRASP metaheuristic. It jointly solves two decisions that are usually treated separately:

1. how many features should be selected for a given dataset and classifier; and
2. which features should compose that subset.

Instead of requiring a fixed value of k, A-GRASPQ-FS searches over a cardinality interval and adapts the probability of choosing each subset size according to the validation performance observed during the search.

The original command-line research prototype used in the ISCC 2026 paper is still available in main.py. Version 1.0 adds a production-oriented Python package in src/agraspqfs/, with a plug-and-play API for scikit-learn workflows.

Associated paper

This repository accompanies the ISCC 2026 paper:

Vagner E. Quincozes, Silvio E. Quincozes, Célio Albuquerque, Diego Passos, and Daniel Mossé. Adaptive Feature Selection with Self-Tuning Subset Size for Intrusion Detection. 31st IEEE Symposium on Computers and Communications (ISCC), Algarve, Portugal, 2026.

If you use this software in academic work, please cite the paper and/or the software release. Citation files are provided in CITATION.cff and docs/CITATION.md.

Highlights in v1.0

• scikit-learn compatible transformer: fit, transform, fit_transform, get_support, get_feature_names_out.
• Works with any scikit-learn compatible classifier as the subset evaluator.
• Joint optimization of subset size and feature composition.
• Presets for different budgets: fast, balanced, robust, and paper.
• In-memory cache to avoid repeated evaluations of the same subset.
• Explicit computational controls: max_evaluations, time_budget, and early_stopping_rounds.
• Exploratory evaluation on a row sample via evaluation_sample_size.
• Final full-data re-evaluation of elite candidate subsets.
• CLI command: agraspqfs.
• Unit tests, examples, GitHub Actions workflow, citation metadata, and release checklist.

Installation

From the repository root:

pip install -e .

For development and tests:

pip install -e ".[dev]"
pytest

Optional XGBoost support:

pip install -e ".[xgboost]"

Minimal usage

from agraspqfs import AGraspQFeatureSelector
from sklearn.naive_bayes import GaussianNB

selector = AGraspQFeatureSelector(
    estimator=GaussianNB(),
    preset="fast",
    min_features=2,
    max_features=15,
    random_state=42,
)

X_selected = selector.fit_transform(X, y)

print(selector.selected_features_)
print(selector.n_features_selected_)
print(selector.best_score_)

Pipeline usage

from agraspqfs import AGraspQFeatureSelector
from sklearn.naive_bayes import GaussianNB
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline

pipe = Pipeline([
    ("fs", AGraspQFeatureSelector(
        estimator=GaussianNB(),
        preset="balanced",
        min_features=2,
        max_features=20,
        random_state=42,
    )),
    ("clf", LogisticRegression(max_iter=2000)),
])

pipe.fit(X_train, y_train)
y_pred = pipe.predict(X_test)

The estimator inside AGraspQFeatureSelector is used to evaluate feature subsets. The classifier after the selector in the pipeline can be the same model or a different one.

CLI usage

agraspqfs   --csv data/hibrid_dataset_GOOSE_train.csv   --target class   --estimator nb   --preset fast   --min-features 2   --max-features 15   --output results/agraspqfs_selection.json   --history-csv results/agraspqfs_history.csv

The CLI prints and saves a JSON report containing selected features, internal validation score, external holdout score, number of evaluations, cache hits, and elapsed time.

Presets

Explicit parameters override preset values.

Example:

selector = AGraspQFeatureSelector(
    preset="fast",
    max_evaluations=100,
    time_budget=60,
    early_stopping_rounds=30,
    random_state=42,
)

Main parameters

Main outputs

After fit, the selector exposes:

selector.selected_features_      # names of selected features
selector.selected_indices_       # integer indices
selector.n_features_selected_    # selected subset size
selector.support_                # boolean mask
selector.best_score_             # best internal score among final candidates
selector.baseline_score_         # internal score with all features
selector.history_                # pandas DataFrame with evaluation trace
selector.size_weights_           # final adaptive cardinality weights
selector.n_evaluations_          # expensive evaluations actually computed
selector.n_cache_hits_           # repeated evaluations avoided by cache
selector.stop_reason_            # completed, budget_exhausted or early_stopping

Examples

Run:

python examples/quickstart.py
python examples/pipeline_example.py
python examples/benchmark_small.py

A minimal notebook is available at examples/agraspqfs_demo.ipynb.

Legacy research prototype

The original scripts are still available:

python main.py -d ereninho -a nb -rcl 10 -is 5 -pq 10 -lc 50 -cc 50

Baseline scripts are also preserved:

python baselines.py -d ereninho -m rfecv -a nb

For new users, prefer the package API or the agraspqfs CLI.

Repository structure

.
├── src/agraspqfs/              # Production package
│   ├── selector.py             # AGraspQFeatureSelector
│   ├── cli.py                  # agraspqfs command
│   └── _version.py
├── tests/                      # Unit tests
├── examples/                   # Quickstart, pipeline, benchmark and notebook examples
├── docs/                       # Documentation and release checklist
├── .github/workflows/          # CI and PyPI publishing workflows
├── main.py                     # Legacy research prototype
├── baselines.py                # Baseline methods
├── utils.py                    # Legacy utilities
├── priority_queue.py           # Legacy queue implementation
├── data/                       # Example/research datasets
├── results/                    # Runtime outputs, ignored except .gitkeep
├── CITATION.cff                # Citation metadata
├── LICENSE                     # MIT license
├── pyproject.toml              # Packaging metadata
└── README.md

Notes on data preprocessing

A-GRASPQ-FS expects the feature matrix passed to fit to be numeric, as is common for scikit-learn estimators and mutual information ranking. For categorical features, use a preprocessing pipeline before the selector, for example OneHotEncoder or ColumnTransformer.

The CLI includes automatic preprocessing for CSV files: numeric columns are scaled and categorical columns are one-hot encoded.

Performance note

A-GRASPQ-FS is a wrapper method and can be computationally expensive because it repeatedly evaluates machine learning models. Use preset="fast", max_evaluations, time_budget, early_stopping_rounds, and evaluation_sample_size to control cost. See docs/PERFORMANCE.md.

License

This project is released under the MIT License. See LICENSE.

The license permits reuse, modification and distribution. For academic use, please cite the associated ISCC 2026 paper and/or the software release as described in CITATION.cff.

Citation

@inproceedings{quincozes2026agraspqfs,
  title     = {Adaptive Feature Selection with Self-Tuning Subset Size for Intrusion Detection},
  author    = {Quincozes, Vagner E. and Quincozes, Silvio E. and Albuquerque, Célio and Passos, Diego and Mossé, Daniel},
  booktitle = {Proceedings of 31st IEEE Symposium on Computers and Communications (ISCC)},
  address   = {Algarve, Portugal},
  year      = {2026}
}