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Authors: Olivier Parisot ; Yoanne Didry ; Thomas Tamisier and Benoît Otjacques

Affiliation: Luxembourg Institute of Science and Technology (LIST), Luxembourg

ISBN: 978-989-758-103-8

Keyword(s): Data Streams, Model Trees, Missing Values Imputation.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Business Analytics ; Data Analytics ; Data Curation ; Data Engineering ; Data Management and Quality ; Data Mining ; Databases and Information Systems Integration ; Datamining ; Enterprise Information Systems ; Health Information Systems ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Predictive Modeling ; Sensor Networks ; Signal Processing ; Soft Computing ; Symbolic Systems

Abstract: 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 several formats:
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

@conference{data15,
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,},
year={2015},
pages={91-96},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005553500910096},
isbn={978-989-758-103-8},
}

TY - CONF

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

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