Training Model Trees on Data Streams with Missing Values

  • Olivier Parisot
  • Yoanne Didry
  • Thomas Tamisier
  • Benoît Otjacques
Conference paper

DOI: 10.1007/978-3-319-30162-4_6

Part of the Communications in Computer and Information Science book series (CCIS, volume 584)
Cite this paper as:
Parisot O., Didry Y., Tamisier T., Otjacques B. (2016) Training Model Trees on Data Streams with Missing Values. In: Helfert M., Holzinger A., Belo O., Francalanci C. (eds) Data Management Technologies and Applications. DATA 2015. Communications in Computer and Information Science, vol 584. Springer, Cham

Abstract

Model trees combine the interpretability of decision trees with the efficiency of multiple linear regressions making them useful in dynamically attaining predictive analysis on data streams. However, missing values within the data streams is an issue during the training phase of a model tree. In this article, we compare different approaches to deal with incomplete streams in order to measure their impact on the resulting model tree in terms of accuracy. Moreover, we propose an online method to estimate and adjust the missing values during the stream processing. To show the results, a prototype has been developed and tested on several benchmarks.

Keywords

Data streams Model trees Missing values imputation 

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Olivier Parisot
    • 1
  • Yoanne Didry
    • 1
  • Thomas Tamisier
    • 1
  • Benoît Otjacques
    • 1
  1. 1.Luxembourg Institute of Science and Technology (LIST)BelvauxLuxembourg

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