loading
Documents

Research.Publish.Connect.

Paper

Authors: Mariele Lanes 1 ; Paula F. Schiavo 1 ; Sidnei F. Pereira Jr. 1 ; Eduardo N. Borges 1 and Renata Galante 2

Affiliations: 1 Universidade Federal do Rio Grande, Brazil ; 2 Universidade Federal do Rio Grande do Sul, Brazil

ISBN: 978-989-758-247-9

Keyword(s): Diversity, Stacking, Ensemble, Classification.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Data Mining ; Databases and Information Systems Integration ; Enterprise Information Systems ; Group Decision Support Systems ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Methodologies and Methods ; Neural Network Software and Applications ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Theory and Methods

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. (More)

PDF ImageFull Text

Download
Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 18.212.90.230

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Lanes, M.; F. Schiavo, P.; F. Pereira Jr., S.; Borges, Eduardo N. 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

@conference{iceis17,
author={Mariele Lanes. and Paula F. Schiavo. and Sidnei F. Pereira Jr.. and Borges, Eduardo N. 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},
}

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 - Borges, Eduardo N.
AU - Galante, R.
PY - 2017
SP - 233
EP - 240
DO - 10.5220/0006291202330240

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.