Preserving Prediction Accuracy on Incomplete Data Streams

Olivier Parisot, Yoanne Didry, Thomas Tamisier, Benoît Otjacques


Model tree is a useful and convenient method for predictive analytics in data streams, combining the interpretability of decision trees with the efficiency of multiple linear regressions. However, missing values within the data streams is a crucial issue in many real world applications. Often, this issue is solved by pre-processing techniques applied prior to the training phase of the model. In this article we propose a new method that proceeds by estimating and adjusting missing values before the model tree creation. A prototype has been developed and experimental results on several benchmarks show that the method improves the accuracy of the resulting model tree.


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Paper Citation

in Harvard Style

Parisot O., Didry Y., Tamisier T. and Otjacques B. (2015). Preserving Prediction Accuracy on Incomplete Data Streams . In Proceedings of 4th International Conference on Data Management Technologies and Applications - Volume 1: DATA, ISBN 978-989-758-103-8, pages 91-96. DOI: 10.5220/0005553500910096

in Bibtex Style

author={Olivier Parisot and Yoanne Didry and Thomas Tamisier and Benoît Otjacques},
title={Preserving Prediction Accuracy on Incomplete Data Streams},
booktitle={Proceedings of 4th International Conference on Data Management Technologies and Applications - Volume 1: DATA,},

in EndNote Style

JO - Proceedings of 4th International Conference on Data Management Technologies and Applications - Volume 1: DATA,
TI - Preserving Prediction Accuracy on Incomplete Data Streams
SN - 978-989-758-103-8
AU - Parisot O.
AU - Didry Y.
AU - Tamisier T.
AU - Otjacques B.
PY - 2015
SP - 91
EP - 96
DO - 10.5220/0005553500910096