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Concept Drift Detector Selection for Hoeffding Adaptive Trees

  • Moana Stirling
  • Yun Sing Koh
  • Philippe Fournier-Viger
  • Sri Devi Ravana
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11320)

Abstract

Dealing with evolving data requires strategies for detecting and quantifying change, and forgetting irrelevant examples during the model revision process. To design an adaptive classifier that is suitable for different types of streams requires us to understand the characteristics of the data stream. Current adaptive classifiers have built-in concept drift detectors used as an estimator at each node. Our research aim is to investigate the usage of different drift detectors for Hoeffding Adaptive Tree (HAT), an adaptive classifier. We proposed three variants of the proposed classifier, called HAT\(_{SEED}\), HAT\(_{HDDM_A}\), and HAT\(_{PHT}\).

Keywords

Adaptive classifiers Concept drift detectors Data streams 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Moana Stirling
    • 1
  • Yun Sing Koh
    • 1
  • Philippe Fournier-Viger
    • 2
  • Sri Devi Ravana
    • 3
  1. 1.The University of AucklandAucklandNew Zealand
  2. 2.Harbin Institute of Technology ShenzhenShenzhenChina
  3. 3.University of MalayaKuala LumpurMalaysia

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