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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Challenges in benchmarking stream learning algorithms with real-world data

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Autor(es):
Souza, Vinicius M. A. [1, 2] ; dos Reis, Denis M. [2] ; Maletzke, Andre G. [2] ; Batista, Gustavo E. A. P. A. [2, 3]
Número total de Autores: 4
Afiliação do(s) autor(es):
[1] Univ New Mexico, Albuquerque, NM 87131 - USA
[2] Univ Sao Paulo, Sao Carlos, SP - Brazil
[3] Univ New South Wales, Sydney, NSW - Australia
Número total de Afiliações: 3
Tipo de documento: Artigo Científico
Fonte: DATA MINING AND KNOWLEDGE DISCOVERY; JUL 2020.
Citações Web of Science: 0
Resumo

Streaming data are increasingly present in real-world applications such as sensor measurements, satellite data feed, stock market, and financial data. The main characteristics of these applications are the online arrival of data observations at high speed and the susceptibility to changes in the data distributions due to the dynamic nature of real environments. The data stream mining community still faces some primary challenges and difficulties related to the comparison and evaluation of new proposals, mainly due to the lack of publicly available high quality non-stationary real-world datasets. The comparison of stream algorithms proposed in the literature is not an easy task, as authors do not always follow the same recommendations, experimental evaluation procedures, datasets, and assumptions. In this paper, we mitigate problems related to the choice of datasets in the experimental evaluation of stream classifiers and drift detectors. To that end, we propose a new public data repository for benchmarking stream algorithms with real-world data. This repository contains the most popular datasets from literature and new datasets related to a highly relevant public health problem that involves the recognition of disease vector insects using optical sensors. The main advantage of these new datasets is the prior knowledge of their characteristics and patterns of changes to adequately evaluate new adaptive algorithms. We also present an in-depth discussion about the characteristics, reasons, and issues that lead to different types of changes in data distribution, as well as a critical review of common problems concerning the current benchmark datasets available in the literature. (AU)

Processo FAPESP: 16/04986-6 - Armadilhas e sensores inteligentes: uma abordagem inovadora para controle de insetos peste e vetores de doenças
Beneficiário:Gustavo Enrique de Almeida Prado Alves Batista
Linha de fomento: Auxílio à Pesquisa - Programa eScience e Data Science - Regular
Processo FAPESP: 17/22896-7 - Detecção dè contexto não-supervisionada Èm fluxos de dados Pará classificação
Beneficiário:Denis Moreira dos Reis
Linha de fomento: Bolsas no Brasil - Doutorado
Processo FAPESP: 18/05859-3 - Armadilhas e sensores inteligentes: uma abordagem inovadora para controle de insetos peste e vetores de doenças
Beneficiário:Vinícius Mourão Alves de Souza
Linha de fomento: Bolsas no Brasil - Pós-Doutorado