Deep-FS

Deep-Feature Selection

The code implement the version of Deep-FS described in:
Aboozar Taherkhani, Georgina Cosma, T. M McGinnity, Deep-FS: A feature selection algorithm for Deep Boltzmann Machines, Neurocomputing, Volume 322,
2018, Pages 22-37, ISSN 0925-2312,
https://doi.org/10.1016/j.neucom.2018.09.040.

Information about the code:

Deep Feature Selection (Deep-FS), is and algorithm capable of removing irrelevant features from large datasets in order to reduce the number of inputs which are modelled during the learning process.

The proposed Deep-FS algorithm utilizes a Deep Boltzmann Machine, and uses knowledge which is acquired during training to remove features at the beginning of the learning process.

The Deep-FS method embeds feature selection in a Restricted Boltzmann Machine which is used for training a Deep Boltzmann Machine. The generative property of the Restricted Boltzmann Machine is used to reconstruct eliminated features and calculate reconstructed errors, in order to evaluate the impact of eliminating features.

Deep-FS is suitable for finding features in large and big datasets which are normally stored in data batches for faster and more efficient processing.

Running Deep-FS will return two options.

Enter 1 to run DeepFS to select features, or

Enter 2 to first select features using DeepFS and then train a Deep Boltzmann machine (DBM) on the selected data.

Available at: https://www.github.com/gcosma/Deep-FS