A Pruning Optimized Fast Learn++NSE Algorithm

A Pruning Optimized Fast Learn++NSE Algorithm

Due to the large number of typical applications, it is very important and urgent to study the fast classification learning of accumulated big data in nonstationary environments. The newly proposed algorithm, named Learn++.NSE, is one of the important research results in this re...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: Yong Chen, Yuquan Zhu, Haifeng Chen, Yan Shen, Zhao Xu
Formato: article
Lenguaje:EN
Publicado: IEEE 2021
Materias:
Ensemble learning
nonstationary environment
classification algorithm
big data mining
Electrical engineering. Electronics. Nuclear engineering
TK1-9971
Acceso en línea:https://doaj.org/article/1c215702f04b4da3bee54d80c7183377
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
id oai:doaj.org-article:1c215702f04b4da3bee54d80c7183377
record_format dspace
spelling oai:doaj.org-article:1c215702f04b4da3bee54d80c71833772021-11-18T00:09:07ZA Pruning Optimized Fast Learn++NSE Algorithm2169-353610.1109/ACCESS.2021.3118568https://doaj.org/article/1c215702f04b4da3bee54d80c71833772021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9562526/https://doaj.org/toc/2169-3536Due to the large number of typical applications, it is very important and urgent to study the fast classification learning of accumulated big data in nonstationary environments. The newly proposed algorithm, named Learn++.NSE, is one of the important research results in this research field. And a pruning version, named Learn++.NSE-Error-based, was given for accumulated big data to improve the learning efficiency. However, the studies have found that the Learn++.NSE-Error-based algorithm often encounters a situation that the newly generated base classifier is pruned in the next integration, which reduces the accuracy of the ensemble classifier. The newly generated base classifier is very important in the next ensemble learning and should be retained. Therefore, the two latest base classifiers are reserved without being pruned, and a new pruning algorithm named NewLearn++.NSE-Error-based was proposed. The experimental results on the generated dataset and the real-world dataset show that NewLearn++.NSE-Error-based can further improve the accuracy of the ensemble classifier under the premise of obtaining the same time complexity as Learn++.NSE algorithm. It is suitable for fast classification learning of long-term accumulated big data.Yong ChenYuquan ZhuHaifeng ChenYan ShenZhao XuIEEEarticleEnsemble learningnonstationary environmentclassification algorithmbig data miningElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 150733-150743 (2021)
institution DOAJ
collection DOAJ
language EN
topic Ensemble learning
nonstationary environment
classification algorithm
big data mining
Electrical engineering. Electronics. Nuclear engineering
TK1-9971
spellingShingle Ensemble learning
nonstationary environment
classification algorithm
big data mining
Electrical engineering. Electronics. Nuclear engineering
TK1-9971
Yong Chen
Yuquan Zhu
Haifeng Chen
Yan Shen
Zhao Xu
A Pruning Optimized Fast Learn++NSE Algorithm
description Due to the large number of typical applications, it is very important and urgent to study the fast classification learning of accumulated big data in nonstationary environments. The newly proposed algorithm, named Learn++.NSE, is one of the important research results in this research field. And a pruning version, named Learn++.NSE-Error-based, was given for accumulated big data to improve the learning efficiency. However, the studies have found that the Learn++.NSE-Error-based algorithm often encounters a situation that the newly generated base classifier is pruned in the next integration, which reduces the accuracy of the ensemble classifier. The newly generated base classifier is very important in the next ensemble learning and should be retained. Therefore, the two latest base classifiers are reserved without being pruned, and a new pruning algorithm named NewLearn++.NSE-Error-based was proposed. The experimental results on the generated dataset and the real-world dataset show that NewLearn++.NSE-Error-based can further improve the accuracy of the ensemble classifier under the premise of obtaining the same time complexity as Learn++.NSE algorithm. It is suitable for fast classification learning of long-term accumulated big data.
format article
author Yong Chen
Yuquan Zhu
Haifeng Chen
Yan Shen
Zhao Xu
author_facet Yong Chen
Yuquan Zhu
Haifeng Chen
Yan Shen
Zhao Xu
author_sort Yong Chen
title A Pruning Optimized Fast Learn++NSE Algorithm
title_short A Pruning Optimized Fast Learn++NSE Algorithm
title_full A Pruning Optimized Fast Learn++NSE Algorithm
title_fullStr A Pruning Optimized Fast Learn++NSE Algorithm
title_full_unstemmed A Pruning Optimized Fast Learn++NSE Algorithm
title_sort pruning optimized fast learn++nse algorithm
publisher IEEE
publishDate 2021
url https://doaj.org/article/1c215702f04b4da3bee54d80c7183377
work_keys_str_mv AT yongchen apruningoptimizedfastlearnx002bx002bnsealgorithm
AT yuquanzhu apruningoptimizedfastlearnx002bx002bnsealgorithm
AT haifengchen apruningoptimizedfastlearnx002bx002bnsealgorithm
AT yanshen apruningoptimizedfastlearnx002bx002bnsealgorithm
AT zhaoxu apruningoptimizedfastlearnx002bx002bnsealgorithm
AT yongchen pruningoptimizedfastlearnx002bx002bnsealgorithm
AT yuquanzhu pruningoptimizedfastlearnx002bx002bnsealgorithm
AT haifengchen pruningoptimizedfastlearnx002bx002bnsealgorithm
AT yanshen pruningoptimizedfastlearnx002bx002bnsealgorithm
AT zhaoxu pruningoptimizedfastlearnx002bx002bnsealgorithm
_version_ 1718425210440384512

Ejemplares similares