Comparative Study of the Performance of the Classification Algorithms of the Apache Spark ML Library

Classification algorithms are widely used in several areas: finance, education, security, medicine, and more. Another use of these algorithms is to support feature extraction techniques. These techniques use classification algorithms to determine the best subset of attributes that support an accepta...

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Detalles Bibliográficos
Autores principales: Camele, Genaro, Hasperué, Waldo, Ronchetti, Franco, Quiroga, Facundo Manuel
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2021
Materias:
Ciencias Informáticas
Big Data
Machine learning
Classification Models
Apache Spark
Spark ML
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/130348
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-130348
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
Big Data
Machine learning
Classification Models
Apache Spark
Spark ML
spellingShingle Ciencias Informáticas
Big Data
Machine learning
Classification Models
Apache Spark
Spark ML
Camele, Genaro
Hasperué, Waldo
Ronchetti, Franco
Quiroga, Facundo Manuel
Comparative Study of the Performance of the Classification Algorithms of the Apache Spark ML Library
topic_facet Ciencias Informáticas
Big Data
Machine learning
Classification Models
Apache Spark
Spark ML
description Classification algorithms are widely used in several areas: finance, education, security, medicine, and more. Another use of these algorithms is to support feature extraction techniques. These techniques use classification algorithms to determine the best subset of attributes that support an acceptable prediction. Currently, a large amount of data is being collected and, as a result, databases are becoming increasingly larger and distributed processing becomes a necessity. In this sense, Spark, and in particular its Spark ML library, is one of the most widely used frameworks for performing classification tasks in large databases. Given that some feature extraction techniques need to execute a classification algorithm a significant number of times, with a different subset of attributes in each run, the performance of these algorithms should be known beforehand so that the overall feature extraction process is carried out in the shortest possible time. In this work, we carry out a comparative study of four Spark ML classification algorithms, measuring predictive power and execution times as a function of the number of attributes in the training dataset.
format Objeto de conferencia
Objeto de conferencia
author Camele, Genaro
Hasperué, Waldo
Ronchetti, Franco
Quiroga, Facundo Manuel
author_facet Camele, Genaro
Hasperué, Waldo
Ronchetti, Franco
Quiroga, Facundo Manuel
author_sort Camele, Genaro
title Comparative Study of the Performance of the Classification Algorithms of the Apache Spark ML Library
title_short Comparative Study of the Performance of the Classification Algorithms of the Apache Spark ML Library
title_full Comparative Study of the Performance of the Classification Algorithms of the Apache Spark ML Library
title_fullStr Comparative Study of the Performance of the Classification Algorithms of the Apache Spark ML Library
title_full_unstemmed Comparative Study of the Performance of the Classification Algorithms of the Apache Spark ML Library
title_sort comparative study of the performance of the classification algorithms of the apache spark ml library
publishDate 2021
url http://sedici.unlp.edu.ar/handle/10915/130348
work_keys_str_mv AT camelegenaro comparativestudyoftheperformanceoftheclassificationalgorithmsoftheapachesparkmllibrary
AT hasperuewaldo comparativestudyoftheperformanceoftheclassificationalgorithmsoftheapachesparkmllibrary
AT ronchettifranco comparativestudyoftheperformanceoftheclassificationalgorithmsoftheapachesparkmllibrary
AT quirogafacundomanuel comparativestudyoftheperformanceoftheclassificationalgorithmsoftheapachesparkmllibrary
bdutipo_str Repositorios
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