| Texto completo | |
| Autor(es): |
Cassales, Guilherme
[1]
;
Gomes, Heitor
[2]
;
Bifet, Albert
[2]
;
Pfahringer, Bernhard
[2]
;
Senger, Hermes
[1]
Número total de Autores: 5
|
| Afiliação do(s) autor(es): | [1] Univ Fed Sao Carlos, Sao Carlos - Brazil
[2] Univ Waikato, Hamilton - New Zealand
Número total de Afiliações: 2
|
| Tipo de documento: | Artigo Científico |
| Fonte: | INFORMATION SCIENCES; v. 580, p. 260-282, NOV 2021. |
| Citações Web of Science: | 0 |
| Resumo | |
Often, machine learning applications have to cope with dynamic environments where data are collected in the form of continuous data streams with potentially infinite length and transient behavior. Compared to traditional (batch) data mining, stream processing algorithms have additional requirements regarding computational resources and adaptability to data evolution. They must process instances incrementally because the data's continuous flow prohibits storing data for multiple passes. Ensemble learning achieved remarkable predictive performance in this scenario. Implemented as a set of (several) individual classifiers, ensembles are naturally amendable for task parallelism. However, the incremental learning and dynamic data structures used to capture the concept drift increase the cache misses and hinder the benefit of parallelism. This paper proposes a mini-batching strategy that can improve memory access locality and performance of several ensemble algorithms for stream mining in multi-core environments. With the aid of a formal framework, we demonstrate that mini-batching can significantly decrease the reuse distance (and the number of cache misses). Experiments on six different state-of-the-art ensemble algorithms applying four benchmark datasets with varied characteristics show speedups of up to 5X on 8-core processors. These benefits come at the expense of a small reduction in predictive performance. (c) 2021 Elsevier Inc. All rights reserved. (AU) | |
| Processo FAPESP: | 18/22979-2 - IoT-SED: segurança e eficiência no transporte de dados na Internet das Coisas |
| Beneficiário: | Daniel Macêdo Batista |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |
| Processo FAPESP: | 19/26702-8 - Tendências em computação de alto desempenho, do gerenciamento de recursos a novas arquiteturas de computadores |
| Beneficiário: | Alfredo Goldman vel Lejbman |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |
| Processo FAPESP: | 15/24461-2 - Estudo de modelos de negócios para federação de serviços para suporte a e-Ciência |
| Beneficiário: | Francisco Vilar Brasileiro |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |