This repository holds all the necessary code to run the very-same experiments described in the paper "A Survey on Metaheuristic Approaches to Feature Selection".

If you use our work to fulfill any of your needs, please cite us:

• data: Folder containing the OPF file format datasets;
• history: Folder for saving the output files, such as .pkl and .txt;
• models
  • classifiers.py: Defines the possible classifiers that can be used;
  • heuristics.py: Defines the possible meta-heuristics that can be used;
  • transfers.py: Defines the possible transfer functions that can be used;
• utils
  • loader.py: Loads OPF file format datasets;
  • optimizer.py: Wraps the optimization task into a single method;
  • outputter.py: Converts the optimization history into readable output files;
  • targets.py: Implements the objective functions to be optimized.

Install all the pre-needed requirements using:

pip install -r requirements.txt

Please download the datasets in the OPF file format and then put them on the data/ folder.

The first step is to select features using a meta-heuristic optimization. To accomplish such a step, one needs to use the following scripts:

python bool_select_features.py -h

or

python select_features.py -h

Note that -h invokes the script helper, which assists users in employing the appropriate parameters.

After conducting the optimization task, one needs to process its history into readable outputs. Please, use the following scripts to accomplish such a procedure:

python bool_process_history.py -h

or

python process_history.py -h

Note that this script converts the .pkl optimization history into readable .txt outputs.

With the readable outputs in hands, one can now classify the testing set using the selected features:

python classify_selected_features.py -h

Additionally, it is possible to perform the classification task over the testing set using baseline features (all features), as follows:

python classify_baseline_features.py -h

Instead of invoking every script to conduct the experiments, it is also possible to use the provided shell scripts, as follows:

./bool_pipeline.sh

or

./pipeline.sh

Such a script will conduct every step needed to accomplish the experimentation used throughout this paper. Furthermore, one can change any input argument that is defined in the script.

We know that we do our best, but it is inevitable to acknowledge that we make mistakes. If you ever need to report a bug, report a problem, talk to us, please do so! We will be available at our bests at this repository.