ml-research 0.3.4

Implementation of Machine Learning algorithms, experiments and utilities.

ML-Research

This repository contains the code developed for all the publications I was involved in. The LaTeX and Python code for generating the paper, experiments’ results and visualizations reported in each paper is available (whenever possible) in the paper’s directory.

Additionally, contributions at the algorithm level are available in the package research.

Installation and Setup

A Python distribution of version 3.7 or higher is required to run this project.

User Installation

If you already have a working installation of numpy and scipy, the easiest way to install scikit-learn is using pip

pip install -U ml-research

The documentation includes more detailed installation instructions.

Installing from source

The following commands should allow you to setup the development version of the project with minimal effort:

# Clone the project.
git clone https://github.com/joaopfonseca/research.git
cd research

# Create and activate an environment
make environment
conda activate research # Adapt this line accordingly if you're not running conda

# Install project requirements and the research package
make requirements

Citing ML-Research

If you use ML-Research in a scientific publication, we would appreciate citations to the following paper:

@article{Fonseca2021,
  doi = {10.3390/RS13132619},
  url = {https://doi.org/10.3390/RS13132619},
  keywords = {SMOTE,active learning,artificial data generation,land use/land cover classification,oversampling},
  year = {2021},
  month = {jul},
  publisher = {Multidisciplinary Digital Publishing Institute},
  volume = {13},
  pages = {2619},
  author = {Fonseca, Joao and Douzas, Georgios and Bacao, Fernando},
  title = {{Increasing the Effectiveness of Active Learning: Introducing Artificial Data Generation in Active Learning for Land Use/Land Cover Classification}},
  journal = {Remote Sensing}
}