Sistema de clasificación paralelo basado en un ensamble de tipo mezcla de expertos
The classification of large amounts of data is a challenging task in machine learning, which only some few classifiers can handle. In this paper we propose a Parallel Classifier based on Mixture of Experts (PCME)to handle this challenging task. The PCME is a novel algorithm since it allows us to classi...
| Autor: | |
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| Tipo de recurso: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2017 |
| País: | México |
| Institución: | Universidad Autónoma Metropolitana |
| Repositorio: | Repositorio Institucional de la UAM Iztapalapa |
| Idioma: | español |
| OAI Identifier: | oai:bindani.izt.uam.mx:v118rd525 |
| Acceso en línea: | https://doi.org/10.24275/uami.v118rd525 |
| Access Level: | acceso abierto |
| Palabra clave: | info:eu-repo/classification/LEM/Procesamiento paralelo (Computación) info:eu-repo/classification/LEM/Parallel processing (Electronic computers) info:eu-repo/classification/LEM/Minerías de datos info:eu-repo/classification/LEM/MIMD (Electronic computers) info:eu-repo/classification/LEM/Data mining info:eu-repo/classification/LEM/MIMD (Computaión) info:eu-repo/classification/cti/1 |
| Sumario: | The classification of large amounts of data is a challenging task in machine learning, which only some few classifiers can handle. In this paper we propose a Parallel Classifier based on Mixture of Experts (PCME)to handle this challenging task. The PCME is a novel algorithm since it allows us to classify large amounts of data with low execution times and performance measures (accuracy, lift, precision and recall) better than other classifiers. In previous work we saw that heterogeneous ensemble models were better but at a explores steep time cost. This work using parallel programming to overcome the time cost problem. We used the MIMD (Multiple Instruction and Multiple Data Stream) architecture, that uses a set of process that communicate via messages. PCME is implemented using parallel schemes of traditional classifiers, for the mixture of experts, and using also a parallel version of a Genetic Algorithm to implement a weighted voting criterion. We performed a series of tests with a set of databases that place the PCME as a very competitive classifier. Keywords: Data Mining and Classification and Classifiers based on Ensemble and Machine Learning and Parallel Computing |
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