Parallel Classification System based on an Ensemble of Mixture of Experts

Benjamín Moreno-Montiel, René MacKinney-Romero

2014

Abstract

The classification of large amounts of data is a challenging problem that only a small number of classification algorithms can handle. In this paper we propose a Parallel Classification System based on an Ensemble of Mixture of Experts (PCEM). The system uses MIMD (Multiple Instruction and Multiple Data Stream) architecture, using a set of process that communicates via messages. PCEM is implemented using parallel schemes of traditional classifiers, for the mixture of experts, and using a parallel version of a Genetic Algorithm to implement a voting weighted criterion. The PCEM is a novel algorithm since it allows us to classify large amounts of data with low execution times and high performance measures, which makes it an excellent tool for in classification of large amounts of data. A series of tests were performed with well known databases that allowed us to measure how PCEM performs with many datasets and how well it does compared with other systems available.

Paper Citation

in Harvard Style

Moreno-Montiel B. and MacKinney-Romero R. (2014). Parallel Classification System based on an Ensemble of Mixture of Experts . In Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-018-5, pages 271-278. DOI: 10.5220/0004828902710278

in Bibtex Style

@conference{icpram14,
author={Benjamín Moreno-Montiel and René MacKinney-Romero},
title={Parallel Classification System based on an Ensemble of Mixture of Experts},
booktitle={Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2014},
pages={271-278},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004828902710278},
isbn={978-989-758-018-5},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Parallel Classification System based on an Ensemble of Mixture of Experts
SN - 978-989-758-018-5
AU - Moreno-Montiel B.
AU - MacKinney-Romero R.
PY - 2014
SP - 271
EP - 278
DO - 10.5220/0004828902710278