Associative Classification in Big Data through a G3P Approach

J. Luna, F. Padillo, S. Ventura

Abstract

The associative classification field includes really interesting approaches for building reliable classifiers and any of these approaches generally work on four different phases (data discretization, pattern mining, rule mining, and classifier building). This number of phases is a handicap when big datasets are analysed. The aim of this work is to propose a novel evolutionary algorithm for efficiently building associative classifiers in Big Data. The proposed model works in only two phases (a grammar-guided genetic programming framework is performed in each phase): 1) mining reliable association rules; 2) building an accurate classifier by ranking and combining the previously mined rules. The proposal has been implemented on Apache Spark to take advantage of the distributed computing. The experimental analysis was performend on 40 well-known datasets and considering 13 algorithms taken from literature. A series of non-parametric tests has also been carried out to determine statistical differences. Results are quite promising in terms of reliability and efficiency on high-dimensional data.

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Paper Citation


in Harvard Style

Luna J., Padillo F. and Ventura S. (2019). Associative Classification in Big Data through a G3P Approach.In Proceedings of the 4th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS, ISBN 978-989-758-369-8, pages 94-102. DOI: 10.5220/0007688400940102


in Bibtex Style

@conference{iotbds19,
author={J. Luna and F. Padillo and S. Ventura},
title={Associative Classification in Big Data through a G3P Approach},
booktitle={Proceedings of the 4th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS,},
year={2019},
pages={94-102},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007688400940102},
isbn={978-989-758-369-8},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 4th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS,
TI - Associative Classification in Big Data through a G3P Approach
SN - 978-989-758-369-8
AU - Luna J.
AU - Padillo F.
AU - Ventura S.
PY - 2019
SP - 94
EP - 102
DO - 10.5220/0007688400940102