GASOM

GASOM: Genetic Algorithm assisted Architecture Learning in Self Organizing Maps

Article detail:

The article has been accepted (to appear, or published) in the Proceedings of the 24th International Conference on Neural Information Processing, held in Guangzhou, China, November 14–18, 2017. (http://www.iconip2017.org/)

Authors:

Ashutosh Saboo, Anant Sharma, Tirtharaj Dash

Data Science Research Group, Department of Computer Science, BITS Pilani, Goa Campus, India

Citation request:

Will be updated soon.

Language:

The GASOM software is written in Python. The GA implementation of the software is parallelized for efficiency.

Dependencies:

1. Pyevolve==0.6rc1
2. matplotlib==2.0.0
3. numpy==1.12.1
4. pandas==0.18.1

Setup:

1. pip install -r requirements.txt
2. Edit the params : datasetpath, number_of_columns_csv, features, dataset_name, type_of_problem, data (Change data numpy array, so that, data contains only the relevant features, without the tags and indices)
3. python train.py > dataset.log (This gives the best possible SOM Map Size for your dataset)
4. Results will be present in dataset_name folder in cwd, along with final stats in dataset.log file.
5. python generate_error_plot.py <dataset_name> (Error plot is generated)
6. Visualise the results

Data sets used for testing our Model:

• Real World Data sets used:

  1. Wine
  2. Iris
  3. Abalone
  4. Car Evaluation
  5. Glass Identification
  6. Sonar

• Synthetic Data sets used:

  1. Corner
  2. CrescentFullMoon
  3. Ginger Breadman
  4. Half Kernal
  5. Outliers
  6. Two Spirals