A Multi-Resolution Learning Approach to Tracking Concept Drift and Recurrent Concepts

Mihai M. Lazarescu

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Paper

A Multi-Resolution Learning Approach to Tracking Concept Drift and Recurrent Concepts

In Proceedings of the 5th International Workshop on Pattern Recognition in Information Systems - Volume 0ICEIS, 52-62, 2005 , Miami, United States

Author: Mihai M. Lazarescu

Affiliation: Faculty of Computer Science, Curtin University, Australia

Abstract: This paper presents a multiple-window algorithm that combines a novel evidence based forgetting method with data prediction to handle different types of concept drift and recurrent concepts. We describe the reasoning behind the algorithm and we compare the performance with the FLORA algorithm on three different problems: the STAGGER concepts problem, a recurrent concept problem and a video surveillance problem.

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Paper citation in several formats:

M. Lazarescu, M. (2005). A Multi-Resolution Learning Approach to Tracking Concept Drift and Recurrent Concepts. In Proceedings of the 5th International Workshop on Pattern Recognition in Information Systems (ICEIS 2005) - PRIS; ISBN 972-8865-28-7, SciTePress, pages 52-62. DOI: 10.5220/0002568900520062

@conference{pris05,
author={Mihai {M. Lazarescu}.},
title={A Multi-Resolution Learning Approach to Tracking Concept Drift and Recurrent Concepts},
booktitle={Proceedings of the 5th International Workshop on Pattern Recognition in Information Systems (ICEIS 2005) - PRIS},
year={2005},
pages={52-62},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002568900520062},
isbn={972-8865-28-7},
}

TY - CONF

JO - Proceedings of the 5th International Workshop on Pattern Recognition in Information Systems (ICEIS 2005) - PRIS
TI - A Multi-Resolution Learning Approach to Tracking Concept Drift and Recurrent Concepts
SN - 972-8865-28-7
AU - M. Lazarescu, M.
PY - 2005
SP - 52
EP - 62
DO - 10.5220/0002568900520062
PB - SciTePress