fastcan

FastCan: A Fast Canonical-Correlation-Based Feature Selection Algorithm

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FastCan is a feature selection method, which has following advantages:

#. Extremely fast.

#. Support unsupervised feature selection.

#. Support multioutput feature selection.

#. Skip redundant features.

#. Evaluate relative usefulness of features.

Check Home Page <https://fastcan.readthedocs.io/en/latest/?badge=latest>_ for more information.

Installation

Install FastCan via PyPi:

• Run pip install fastcan

Or via conda-forge:

• Run conda install -c conda-forge fastcan

Getting Started

from fastcan import FastCan X = [[ 0.87, -1.34, 0.31 ], ... [-2.79, -0.02, -0.85 ], ... [-1.34, -0.48, -2.55 ], ... [ 1.92, 1.48, 0.65 ]] y = [[0, 0], [1, 1], [0, 0], [1, 0]] # Multioutput feature selection selector = FastCan(n_features_to_select=2, verbose=0).fit(X, y) selector.get_support() array([ True, True, False]) selector.get_support(indices=True) # Sorted indices array([0, 1]) selector.indices_ # Indices in selection order array([1, 0], dtype=int32) selector.scores_ # Scores for selected features in selection order array([0.91162413, 0.71089547])

Here Feature 2 must be included

selector = FastCan(n_features_to_select=2, indices_include=[2], verbose=0).fit(X, y)

We can find the feature which is useful when working with Feature 2

selector.indices_ array([2, 0], dtype=int32) selector.scores_ array([0.34617598, 0.95815008])

Citation

FastCan is a Python implementation of the following papers.

If you use the h-correlation method in your work please cite the following reference:

.. code:: bibtex

@article{ZHANG2022108419, title = {Orthogonal least squares based fast feature selection for linear classification}, journal = {Pattern Recognition}, volume = {123}, pages = {108419}, year = {2022}, issn = {0031-3203}, doi = {https://doi.org/10.1016/j.patcog.2021.108419}, url = {https://www.sciencedirect.com/science/article/pii/S0031320321005951}, author = {Sikai Zhang and Zi-Qiang Lang}, keywords = {Feature selection, Orthogonal least squares, Canonical correlation analysis, Linear discriminant analysis, Multi-label, Multivariate time series, Feature interaction}, }

If you use the eta-cosine method in your work please cite the following reference:

.. code:: bibtex

@article{ZHANG2025111895, title = {Canonical-correlation-based fast feature selection for structural health monitoring}, journal = {Mechanical Systems and Signal Processing}, volume = {223}, pages = {111895}, year = {2025}, issn = {0888-3270}, doi = {https://doi.org/10.1016/j.ymssp.2024.111895}, url = {https://www.sciencedirect.com/science/article/pii/S0888327024007933}, author = {Sikai Zhang and Tingna Wang and Keith Worden and Limin Sun and Elizabeth J. Cross}, keywords = {Multivariate feature selection, Filter method, Canonical correlation analysis, Feature interaction, Feature redundancy, Structural health monitoring}, }