Pathfinder

Pathfinder is a innovative Feature Selection method based on the Ant Colony Optimization (ACO) algorithm.

Pathfinder is in continuous developing and improving, so the actual version is composed of a filter approach and a randomized search heuristic.

Requirements

Pathfinder requires Python 3. It also depends on the following Python packages:

• NumPy.
• Scikit-learn.
• Scipy.
• Sphinx if you want to generate documentation.

Documentation

Pathfinder is fully documented in its Github Pages. In addition, in doc subfolder, the Make files contains a rule to generate Sphinx documentation in the doc/build/html folder.

Usage

In test subfolder there is a test.py file that runs the application on a dataset. This program can work with two dataset versions:

• A dataset divided in 4 files: training data, testing data, training classes, and testing classes. This files must be in MATLAB format (.mat). Usage:

  • $ python test.py mat dataTrainingDataset classTrainingDataset dataTestingDataset classTestingDataset numberAnts numberColonies numberFeatures

• A dataset in only one file: all data together with the classes labels in the last column of the dataset. This file must be in comma separated format (.csv). Usage:

  • $ python test.py csv dataset numberAnts numberColonies numberFeatures

where:

• .mat version:
  • dataTrainingDataset = Path to the .mat file of data of the training dataset.
  • classTrainingDataset = Path to the .mat file of corresponding classes of the trainin dataset.
  • dataTestingDataset = Path to the .mat file of data of the testing dataset.
  • classTestingDataset = Path to the .mat file of corresponding classes of the testing dataset.
• .csv version:
  • dataset = Path to the .csv file of the entire dataset.
  • numberAnts = Number of ants for the algorithm.
  • numberColonies = Number of colonies for the algorithm.
  • numberFeatures = Number of features to be selected.

Publications

Conferences

1. A. Ortega, J.J. Escobar, J. Ortega, J. González, A. Alcayde, J. Munilla, and M. Damas. Performance Study of Ant Colony Optimization for Feature Selection in EEG Classification. In: International Conference on Bioengineering and Biomedical Signal and Image Processing. BIOMESIP'2021. Gran Canaria, Spain: Springer, July 2021, pp. 323-336. DOI: 10.1007/978-3-030-88163-4_28

2. A. Ortega, J.J. Escobar, M. Damas, A. Ortiz, and J. González. Ant Colony Optimization for Feature Selection via a Filter-Randomized Search Heuristic. In Genetic and Evolutionary Computation Conference Companion. GECCO'2022. Boston, MA, USA: ACM, July 2022. DOI: 10.1145/3520304.3528817

Funding

This work has been funded by:

• Spanish Ministerio de Ciencia, Innovación y Universidades under grant number PGC2018-098813-B-C31.
• European Regional Development Fund (ERDF).

License

GNU GPLv3.

Copyright

Pathfinder © 2021 EFFICOMP.