On the adaptability of ensemblemethods for distributed classificationsystems: A comparative analysis

Villaverde San José, Mónica and Aledo Ortega, David and Pérez, David and Moreno González, Félix Antonio (2019). On the adaptability of ensemblemethods for distributed classificationsystems: A comparative analysis. "International Journal of Distributed Sensor Networks", v. 15 (n. 7); pp. 1-19. ISSN 1550-1477. https://doi.org/10.1177/1550147719865505.

Description

Title: On the adaptability of ensemblemethods for distributed classificationsystems: A comparative analysis
Author/s:
  • Villaverde San José, Mónica
  • Aledo Ortega, David
  • Pérez, David
  • Moreno González, Félix Antonio
Item Type: Article
Título de Revista/Publicación: International Journal of Distributed Sensor Networks
Date: 2019
ISSN: 1550-1477
Volume: 15
Subjects:
Freetext Keywords: Ensemble methods; wireless sensor networks; artificial neural networks; voting algorithms; classifier ensemble
Faculty: E.T.S.I. Industriales (UPM)
Department: Ingeniería Mecánica
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

In this work, a two-stage architecture is used to analyze the information collected from several sensors. The first stage makes classifications from partial information of the entire target (i.e. from different points of view or from different kind of measures) using a simple artificial neural network as a classifier. In addition, the second stage aggregates all the estimations given by the ensemble in order to obtain the final classification. Four different ensembles methods are compared in the second stage: artificial neural network, plurality majority, basic weighted majority, and stochastic weighted majority. However, not only reliability is an important factor but also adaptation is critical when the ensemble is working in changing environments. Therefore, the artificial neural network and the plurality majority algorithm are compared against our two proposed adaptive algorithms. Unlike artificial neural network, majority methods do not require previous training. The effects of improving the first stage and how the system behaves when different perturbations are presented have been measured. Results have been obtained from two applications: a realistic one and another simpler one, with more training examples for a more accurate comparison. These results show that artificial neural network is the most accurate proposal, whereas the most innovative proposed stochastic weighted voting is the most adaptive one.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainFPU13/04424UnspecifiedUnspecifiedUnspecified
Universidad Politécnica de MadridRR01/2015UnspecifiedUnspecifiedPrograma propio

More information

Item ID: 66868
DC Identifier: http://oa.upm.es/66868/
OAI Identifier: oai:oa.upm.es:66868
DOI: 10.1177/1550147719865505
Official URL: https://journals.sagepub.com/doi/full/10.1177/1550147719865505
Deposited by: Memoria Investigacion
Deposited on: 28 Apr 2021 13:37
Last Modified: 28 Apr 2021 13:37
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