GMM-VRD: A Gaussian Mixture Model for Dealing With Virtual and Real Concept Drifts -

Usage

# Cloning the repository
git clone https://github.com/GustavoHFMO/GMM-VRD.git

# Acessing the repository
cd GMM-VRD

# Installing the dependencies
pip install -r requirements.txt

GMM with training in batch

The module GMM_batch.py shows how to train a GMM for classification using a batch of observations, and also plots the generated model.

# Running the code
python GMM_batch.py

Result

GMM-VRD to handle virtual and real drifts

The module GMM_online.py executes the algorithms described below in real and synthetic datasets.

# Running the code
python GMM_online.py

GMM-VRD:

Oliveira, Gustavo HFM, Leandro L. Minku, and Adriano LI Oliveira. "GMM-VRD: A Gaussian Mixture Model for Dealing With Virtual and Real Concept Drifts." 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 2019.

Dynse:

P. R. Almeida, L. S. Oliveira, A. S. Britto Jr, and R. Sabourin, “Adapting dynamic classifier selection for concept drift,” Expert Systems with Applications, vol. 104, pp. 67–85, 2018.

IGMM-CD:

L. S. Oliveira and G. E. Batista, “Igmm-cd: a gaussian mixture classification algorithm for data streams with concept drifts,” in BRACIS, 2015 Brazilian Conference on. IEEE, 2015, pp. 55–61

Result

License

This project is under a GNU General Public License (GPL) Version 3. See LICENSE for more information.