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The Impact of the Diversity on Multiple Classifier System Performance - Identifying Changes in the Amount of Fuel in the Fleet Management System

Topics: Change Detection Problems; Decision Support Systems; Industrial Networks and Automation; Intelligent Fault Detection and Identification; Machine Learning in Control Applications; Real-Time Systems Control; Vision, Recognition and Reconstruction

Authors: Rafał Łysiak and Marek Kurzyński

Affiliation: Wroclaw University of Technology, Poland

ISBN: 978-989-758-039-0

Keyword(s): Multiple Classifier System, Diversity, Imbalanced Data, Random Reference Classifier, Dynamic Ensemble Selection, Classifier Competence.

Related Ontology Subjects/Areas/Topics: Business Analytics ; Change Detection ; Data Engineering ; Decision Support Systems ; Industrial Networks and Automation ; Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Intelligent Fault Detection and Identification ; Machine Learning in Control Applications ; Real-Time Systems Control ; Robotics and Automation ; Signal Processing, Sensors, Systems Modeling and Control ; Vision, Recognition and Reconstruction

Abstract: When it comes to the use of any recognition systems in the real world environment, it turns out that the reality differs from the theory. There is an assumption that the distribution of the incoming data will be at least similar to the distribution of the data, which were used during the learning process and that learning dataset represents the entire space of the problem. In fact, the incoming data differ from the training set and usually cover only a part of the feature space. Very often we have to deal with imbalanced datasets which leads to underfitting of classifiers in the final ensemble. In this paper we present the Multiple Classifier System based on Random Reference Classifier in the problem of fuel level change detection in the fleet management systems. The ensemble selection process uses probabilistic measures of competence and diversity at the same time. We compare different methods to determine the diversity within the ensemble.

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Paper citation in several formats:
Łysiak R. and Kurzyński M. (2014). The Impact of the Diversity on Multiple Classifier System Performance - Identifying Changes in the Amount of Fuel in the Fleet Management System.In Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-039-0, pages 348-354. DOI: 10.5220/0005066703480354

@conference{icinco14,
author={Rafał Łysiak and Marek Kurzyński},
title={The Impact of the Diversity on Multiple Classifier System Performance - Identifying Changes in the Amount of Fuel in the Fleet Management System},
booktitle={Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2014},
pages={348-354},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005066703480354},
isbn={978-989-758-039-0},
}

TY - CONF

JO - Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - The Impact of the Diversity on Multiple Classifier System Performance - Identifying Changes in the Amount of Fuel in the Fleet Management System
SN - 978-989-758-039-0
AU - Łysiak R.
AU - Kurzyński M.
PY - 2014
SP - 348
EP - 354
DO - 10.5220/0005066703480354

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