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Authors: Kent Munthe Caspersen ; Martin Bjeldbak Madsen ; Andreas Berre Eriksen and Bo Thiesson

Affiliation: Aalborg University, Denmark

ISBN: 978-989-758-222-6

Keyword(s): Machine Learning, Multi-class Classification, Hierarchical Classification, Tree Distance Measures, Multi-output Regression, Multidimensional Scaling, Process Automation, UNSPSC.

Related Ontology Subjects/Areas/Topics: Applications ; Classification ; Data Engineering ; Economics, Business and Forecasting Applications ; Embedding and Manifold Learning ; Information Retrieval ; Ontologies and the Semantic Web ; Pattern Recognition ; Software Engineering ; Theory and Methods

Abstract: In this paper, we explore the problem of classification where class labels exhibit a hierarchical tree structure. Many multiclass classification algorithms assume a flat label space, where hierarchical structures are ignored. We take advantage of hierarchical structures and the interdependencies between labels. In our setting, labels are structured in a product and service hierarchy, with a focus on spend analysis. We define a novel distance measure between classes in a hierarchical label tree. This measure penalizes paths though high levels in the hierarchy. We use a known classification algorithm that aims to minimize distance between labels, given any symmetric distance measure. The approach is global in that it constructs a single classifier for an entire hierarchy by embedding hierarchical distances into a lower-dimensional space. Results show that combining our novel distance measure with the classifier induces a trade-off between accuracy and lower hierarchical distances on mis classifications. This is useful in a setting where erroneous predictions vastly change the context of a label. (More)

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Paper citation in several formats:
Munthe Caspersen, K.; Bjeldbak Madsen, M.; Berre Eriksen, A. and Thiesson, B. (2017). A Hierarchical Tree Distance Measure for Classification.In Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-222-6, pages 502-509. DOI: 10.5220/0006198505020509

@conference{icpram17,
author={Kent Munthe Caspersen. and Martin Bjeldbak Madsen. and Andreas Berre Eriksen. and Bo Thiesson.},
title={A Hierarchical Tree Distance Measure for Classification},
booktitle={Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2017},
pages={502-509},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006198505020509},
isbn={978-989-758-222-6},
}

TY - CONF

JO - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - A Hierarchical Tree Distance Measure for Classification
SN - 978-989-758-222-6
AU - Munthe Caspersen, K.
AU - Bjeldbak Madsen, M.
AU - Berre Eriksen, A.
AU - Thiesson, B.
PY - 2017
SP - 502
EP - 509
DO - 10.5220/0006198505020509

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