A Novel Pre-processing Method for Enhancing Classification Over Sensor Data Streams Using Subspace Probability Detection

  1. Yan Zhong 1
  2. Tengyue Li 1
  3. Simon Fong 1
  4. Xuqi Li 2
  5. Tallón-Ballesteros, Antonio J. 3
  6. Sabah Mohammed 4
  1. 1 University of Macau
    info

    University of Macau

    Macao, Macao

    ROR https://ror.org/01r4q9n85

  2. 2 University of Edinburgh
    info

    University of Edinburgh

    Edimburgo, Reino Unido

    ROR https://ror.org/01nrxwf90

  3. 3 Universidad de Huelva
    info

    Universidad de Huelva

    Huelva, España

    ROR https://ror.org/03a1kt624

  4. 4 Lakehead University
    info

    Lakehead University

    Thunder Bay, Canadá

    ROR https://ror.org/023p7mg82

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Libro:
Hybrid Artificial Intelligent Systems: 16th International Conference, HAIS 2021. Bilbao, Spain. September 22–24, 2021. Proceedings
  1. Hugo Sanjurjo González (coord.)
  2. Iker Pastor López (coord.)
  3. Pablo García Bringas (coord.)
  4. Héctor Quintián (coord.)
  5. Emilio Corchado (coord.)

Editorial: Springer International Publishing AG

ISBN: 978-3-030-86271-8,  978-3-030-86270-1

Año de publicación: 2021

Páginas: 38-49

Congreso: Hybrid Artificial Intelligent Systems (HAIS) (16. 2021. Bilbao)

Tipo: Aportación congreso

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Resumen

The rapid development of the Internet of Things has led to the widespread use of sensors in everyday life. Large amounts of data through sensing devices are collected. The data quantity is massive, but most of the data are repetitive and noisy.When traditional classification algorithms are used for classifying sensor data, the performance of the model is often poor because the classification granularity is too small. In order to better data mine the knowledge from the Internet of Things data which is a kind of big data, a new classification model based on subspace probability detection is proposed. This model can be well integrated with traditional data mining algorithms, and the performance on sensor data mining is greatly improved.

Fuente de los datos: Dialnet