Several libraries and machine-learning frameworks have been proposed in the literature to support users in the process of defining the most appropriate methods for their applications. However, many frameworks have limitations including the lack of flexibility to include novel proposed descriptors and machine learning methods, and specially, the inability to reuse previous experiments and learn from them.
In this article published in Future Generation Computer Systems, the authors propose Kuaa, a workflow-based framework that can be used for designing, deploying, and executing machine learning experiments in an automated fashion. This framework is able to provide 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. Kuaa also is capable of providing users with the recommendation of machine-learning workflows. The use of recommendations allows users to identify, evaluate, and possibly reuse previously defined successful solutions. The authors propose the use of similarity measures (e.g., Jaccard, Sørensen, and Jaro–Winkler) and learning-to-rank methods (LRAR) in the implementation of the recommendation service.
Experimental results show that Jaro–Winkler yields the highest effectiveness performance with comparable results to those observed for LRAR, presenting the best alternative machine learning experiments to the user. In both cases, the recommendations performed are very promising and the developed framework might help users in different daily exploratory machine learning tasks.
Kuaa Code: https://github.com/rafaelwerneck/kuaa
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, Future Generation Computer Systems, Volume 78, 2018, Pages 59-76, ISSN 0167-739X, http://dx.doi.org/10.1016/j.future.2017.06.013.