An Analysis of the Impact of Diversity on Stacking Supervised Classifiers

Mariele Lanes, Paula F. Schiavo, Sidnei F. Pereira Jr., Eduardo N. Borges, Renata Galante

Abstract

Due to the growth of research in pattern recognition area, the limits of the techniques used for the classification task are increasingly tested. Thus, it is clear that specialized and properly configured classifiers are quite effective. However, it is not a trivial task to choose the most appropriate classifier for deal with a particular problem and set it up properly. In addition, there is no optimal algorithm to solve all prediction problems. Thus, in order to improve the result of the classification process, some techniques combine the knowledge acquired by individual learning algorithms aiming to discover new patterns not yet identified. Among these techniques, there is the stacking strategy. This strategy consists in the combination of outputs of base classifiers, induced by several learning algorithms using the same dataset, by means of another classifier called meta-classifier. This paper aims to verify the relation between the classifiers diversity and the quality of stacking. We have performed a lot of experiments which results show the impact of multiple diversity measures on the gain of stacking.

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Paper Citation


in Harvard Style

Lanes M., F. Schiavo P., F. Pereira Jr. S., N. Borges E. and Galante R. (2017). An Analysis of the Impact of Diversity on Stacking Supervised Classifiers . In Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-247-9, pages 233-240. DOI: 10.5220/0006291202330240


in Bibtex Style

@conference{iceis17,
author={Mariele Lanes and Paula F. Schiavo and Sidnei F. Pereira Jr. and Eduardo N. Borges and Renata Galante},
title={An Analysis of the Impact of Diversity on Stacking Supervised Classifiers},
booktitle={Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2017},
pages={233-240},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006291202330240},
isbn={978-989-758-247-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - An Analysis of the Impact of Diversity on Stacking Supervised Classifiers
SN - 978-989-758-247-9
AU - Lanes M.
AU - F. Schiavo P.
AU - F. Pereira Jr. S.
AU - N. Borges E.
AU - Galante R.
PY - 2017
SP - 233
EP - 240
DO - 10.5220/0006291202330240