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

• Yan Zhong ^[1] ; Tengyue Li ^[1] ; Simon Fong ^[1] ; Xuqi Li ^[2] ; Tallón-Ballesteros, Antonio J. ^[3] ; Sabah Mohammed ^[4]
  1. [1] University of Macau

     University of Macau

     RAE de Macao (China)

     
  2. [2] University of Edinburgh

     University of Edinburgh

     Reino Unido

     
  3. [3] Universidad de Huelva

     Universidad de Huelva

     Huelva, España

     
  4. [4] Lakehead University

     Lakehead University

     Canadá

     

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• Localización: Hybrid Artificial Intelligent Systems: 16th International Conference, HAIS 2021. Bilbao, Spain. September 22–24, 2021. Proceedings / coord. por Hugo Sanjurjo González, Iker Pastor López , Pablo García Bringas , Héctor Quintián Pardo , Emilio Santiago Corchado Rodríguez , 2021, ISBN 978-3-030-86271-8, págs. 38-49
• Idioma: inglés
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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.