Kuaa is a workflow-based framework that can be used for designing, deploying, and executing machine learning experiments. This framework provides a standardized environment for exploratory analysis of machine learning solutions, as it supports the evaluation of feature descriptors, normalizers, classifiers, and fusion approaches in a wide range of tasks involving machine learning.

Rafael de Oliveira Werneck, Waldir Rodrigues de Almeida, Bernardo Vecchia Stein, Daniel Vatanabe Pazinato, Pedro Ribeiro Mendes Júnior, Otávio Augusto Bizetto Penatti, Anderson Rocha, Ricardo da Silva Torres, Kuaa: A unified framework for design, deployment, execution, and recommendation of machine learning experiments, In Future Generation Computer Systems, Volume 78, Part 1, 2018, Pages 59-76, ISSN 0167-739X, https://doi.org/10.1016/j.future.2017.06.013. Bibtex

e-mail: rafael.werneck@ic.unicamp.br

Kuaa dependencies are listed in Kuaa_install_dependencies_v1.3.1.sh.

bash Kuaa_install_dependencies_v1.3.1.sh.

To execute Kuaa using its interface:

python initInterface.py

Kuaa can be also executed from the terminal, just need the path to the XML experiment file.

python initFramework.py <xml_experiment_path>