Interpretabilidade de modelos de aprendizado de máquina por meio de uma rede de autoencoders
Abstract
In recent years, with the increasing ability of computers to process vector computations, deep learning models have spread with the advent of new machine learning algorithms. These models, which are used in a wide variety of business areas, can be viewed as a black box that can neither be debugged nor audited. In this paper we will introduce a tree-based architecture which, because it is hierarchical, is more transparent and interpretable. The presented work is in its initial stage and the results obtained so far are still preliminary.
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Birla, D.Basics of Autoencoders. 2019. Disponível em: <https://medium.com/@birla.deepak26/autoencoders-76bb49ae6a8f>.
XIAO, Han; RASUL, Kashif; VOLLGRAF, Roland. Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747, 2017.
Riccardo Miotto, Fei Wang, Shuang Wang, Xiaoqian Jiang, Joel T Dudley, Deep learning for healthcare: review, opportunities and challenges, Briefings in Bioinformatics, Volume 19, Issue 6, November 2018, Pages 1236–1246, https://doi.org/10.1093/bib/bbx044
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Badr, Will. Auto-Encoder: What Is It? And What Is It Used For? (Part 1), 2019. Disponível em: [link].
Published
2021-10-25
How to Cite
CERQUEIRA, Hélisson Oliveira Magalhães; MATOS, Leonardo Nogueira.
Interpretabilidade de modelos de aprendizado de máquina por meio de uma rede de autoencoders. In: REGIONAL SCHOOL ON COMPUTING OF BAHIA, ALAGOAS, AND SERGIPE (ERBASE), 21. , 2021, Maceió.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 28-36.
DOI: https://doi.org/10.5753/erbase.2021.20053.
