CLASSIFICATION BY SUCCESSIVE NEIGHBORHOOD

David Grosser, Henri Ralambondrainy, Noel Conruyt

Abstract

Formalization of scientific knowledge in life sciences by experts in biology or Systematics produces arborescent representations whose values could be present, absent or unknown. To improve the robustness of the classification process of those complex objects, often partially described, we propose a new classification method which is iterative, interactive and semi-directed. It combines inductive techniques for the choice of discriminating variables and search for nearest neighbors based on various similarity measures which take into account structures and values of the objects for the neighborhood computation.

References

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  3. Quinlan, J. (1986). Induction of decision trees. Machine Learning, 1:81-106.
  4. Sneath, H. and Sokal, R. (1973). Numerical Taxonomy. W.H. Freeman.
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Paper Citation


in Harvard Style

Grosser D., Ralambondrainy H. and Conruyt N. (2009). CLASSIFICATION BY SUCCESSIVE NEIGHBORHOOD . In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2009) ISBN 978-989-674-011-5, pages 288-291. DOI: 10.5220/0002299002880291


in Bibtex Style

@conference{kdir09,
author={David Grosser and Henri Ralambondrainy and Noel Conruyt},
title={CLASSIFICATION BY SUCCESSIVE NEIGHBORHOOD},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2009)},
year={2009},
pages={288-291},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002299002880291},
isbn={978-989-674-011-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2009)
TI - CLASSIFICATION BY SUCCESSIVE NEIGHBORHOOD
SN - 978-989-674-011-5
AU - Grosser D.
AU - Ralambondrainy H.
AU - Conruyt N.
PY - 2009
SP - 288
EP - 291
DO - 10.5220/0002299002880291