Objective Measures Ensemble in Associative Classifiers

Maicon Dall’Agnol, Veronica Oliveira de Carvalho

Abstract

Associative classifiers (ACs) are predictive models built based on association rules (ARs). Model construction occurs in steps, one of them aimed at sorting and pruning a set of rules. Regarding ordering, usually objective measures (OMs) are used to rank the rules. The aim of this work is exactly sorting. In the proposals found in the literature, the OMs are generally explored separately. The only work that explores the aggregation of measures in the context of ACs is (Silva and Carvalho, 2018), where multiple OMs are considered at the same time. To do so, (Silva and Carvalho, 2018) use the aggregation solution proposed by (Bouker et al., 2014). However, although there are many works in the context of ARs that investigate the aggregate use of OMs, all of them have some bias. Thus, this work aims to evaluate the aggregation of measures in the context of ACs considering another perspective, that of an ensemble of classifiers.

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Paper Citation


in Harvard Style

Dall’Agnol M. and Oliveira de Carvalho V. (2020). Objective Measures Ensemble in Associative Classifiers.In Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-423-7, pages 83-90. DOI: 10.5220/0009321600830090


in Bibtex Style

@conference{iceis20,
author={Maicon Dall’Agnol and Veronica Oliveira de Carvalho},
title={Objective Measures Ensemble in Associative Classifiers},
booktitle={Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2020},
pages={83-90},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009321600830090},
isbn={978-989-758-423-7},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - Objective Measures Ensemble in Associative Classifiers
SN - 978-989-758-423-7
AU - Dall’Agnol M.
AU - Oliveira de Carvalho V.
PY - 2020
SP - 83
EP - 90
DO - 10.5220/0009321600830090