Výsledky vyhledávání - "María José Basgall"

Akademický článek

Analysis and design of scalable pre-processing techniques of instances for imbalanced Big Data problems. Applications in humanitarian emergencies situations.

Autor: María José Basgall

Publikováno v: Journal of Computer Science and Technology, Vol 22, Iss 2, Pp e15-e15 (2022)

This thesis addresses the distributed and scalable pre-processing of Big Data sets, in order to obtain good quality data, known as Smart Data. Particularly, it focuses on classification problems, and on addressing the following characteristics: (a) i

Externí odkaz: https://doaj.org/article/165e4810a4c0489aa95e4dd9a369af6d

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Akademický článek

SMOTE-BD: An Exact and Scalable Oversampling Method for Imbalanced Classification in Big Data

Autor: María José Basgall, Waldo Hasperué, Marcelo Naiouf, Alberto Fernández, Francisco Herrera

Publikováno v: Journal of Computer Science and Technology, Vol 18, Iss 03, Pp e23-e23 (2018)

The volume of data in today's applications has meant a change in the way Machine Learning issues are addressed. Indeed, the Big Data scenario involves scalability constraints that can only be achieved through intelligent model design and the use of d

Externí odkaz: https://doaj.org/article/f723576555f841bcb87803ac7c43ea25

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Akademický článek

Data stream treatment using sliding windows with MapReduce

Autor: María José Basgall, Waldo Hasperué, Marcelo Naiouf

Publikováno v: Journal of Computer Science and Technology, Vol 16, Iss 02, Pp 76-83 (2016)

Knowledge Discovery in Databases (KDD) techniques present limitations when the volume of data to process is very large. Any KDD algorithm needs to do several iterations on the complete set of data in order to carry out its work. For continuous data s

Externí odkaz: https://doaj.org/article/0ae62d00f0924941ac68959f8ed27a04

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FDR2-BD: A Fast Data Reduction Recommendation Tool for Tabular Big Data Classification Problems

Autor: María José Basgall, Marcelo Naiouf, Alberto Fernández

Publikováno v: Electronics
Volume 10
Issue 15
CONICET Digital (CONICET)
Consejo Nacional de Investigaciones Científicas y Técnicas
instacron:CONICET
Electronics, Vol 10, Iss 1757, p 1757 (2021)
Digibug. Repositorio Institucional de la Universidad de Granada
instname

In this paper, a methodological data condensation approach for reducing tabular big datasets in classification problems is presented, named FDR2-BD. The key of our proposal is to analyze data in a dual way (vertical and horizontal), so as to provide

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5eeb69c4dd9f1d4a35f0251021075154
https://doi.org/10.3390/electronics10151757

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An Analysis of Local and Global Solutions to Address Big Data Imbalanced Classification: A Case Study with SMOTE Preprocessing

Autor: Francisco Herrera, Alberto Fernández, Waldo Hasperué, Marcelo Naiouf, María José Basgall

Publikováno v: Communications in Computer and Information Science ISBN: 9783030277123
JCC&BD

Addressing the huge amount of data continuously generated is an important challenge in the Machine Learning field. The need to adapt the traditional techniques or create new ones is evident. To do so, distributed technologies have to be used to deal

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::085e8ff13d82904832989038940f1373
https://doi.org/10.1007/978-3-030-27713-0_7

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SMOTE-BD: un método de sobremuestreo exacto y escalable para la clasificación no balanceada en big data

Autor: Francisco Herrera, Waldo Hasperué, Marcelo Naiouf, Alberto Fernández, María José Basgall

Publikováno v: Journal of Computer Science and Technology, Vol 18, Iss 03, Pp e23-e23 (2018)
CONICET Digital (CONICET)
Consejo Nacional de Investigaciones Científicas y Técnicas
instacron:CONICET
SEDICI (UNLP)
Universidad Nacional de La Plata
instacron:UNLP

The volume of data in today’s applications has meant a change in the way Machine Learning issues are addressed. Indeed, the Big Data scenario involves scalability constraints that can only be achieved through intelligent model design and the use of

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7764856345ee77ceee33e6deb4efafe5
http://journal.info.unlp.edu.ar/JCST/article/view/1122

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Some stylized facts of the Bitcoin market

Autor: María José Basgall, Marcelo Naiouf, Aurelio F. Bariviera, Waldo Hasperué

Publikováno v: CIC Digital (CICBA)
Comisión de Investigaciones Científicas de la Provincia de Buenos Aires
instacron:CICBA
CONICET Digital (CONICET)
Consejo Nacional de Investigaciones Científicas y Técnicas
instacron:CONICET

In recent years a new type of tradable assets appeared, generically known as cryptocurrencies. Among them, the most widespread is Bitcoin. Given its novelty, this paper investigates some statistical properties of the Bitcoin market. This study compar

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e96a59bce96fb2bde21dde40fe752ff5
https://digital.cic.gba.gob.ar/handle/11746/8578

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