NACOD: A Naïve Associative Classifier for Online Data

Analyzing data in real time constitutes a challenge nowadays, due to the constant generation of data from different sources. To deal to such streams of data, in this paper we propose a novel decision-making algorithm within the associative approach. The proposed algorithm, named Naïve As...

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Main Authors: Yenny Villuendas-Rey, Javier A. Hernandez-Castano, Oscar Camacho-Nieto, Cornelio Yanez-Marquez, Itzama Lopez-Yanez
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/8805380/
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author Yenny Villuendas-Rey
Javier A. Hernandez-Castano
Oscar Camacho-Nieto
Cornelio Yanez-Marquez
Itzama Lopez-Yanez
author_facet Yenny Villuendas-Rey
Javier A. Hernandez-Castano
Oscar Camacho-Nieto
Cornelio Yanez-Marquez
Itzama Lopez-Yanez
author_sort Yenny Villuendas-Rey
collection DOAJ
description Analyzing data in real time constitutes a challenge nowadays, due to the constant generation of data from different sources. To deal to such streams of data, in this paper we propose a novel decision-making algorithm within the associative approach. The proposed algorithm, named Naïve Associative Classifier for Online Data (NACOD), is able to deal with hybrid as well as with incomplete data. In addition, NACOD is transparent and transportable, which makes it a very useful decision-maker in environments that require such properties. The numerical experiments carried out show the effectiveness of NACOD.
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spelling doaj.art-f5e9f0f087cf4ee6a5dfb471b6728e0b2022-12-21T22:00:57ZengIEEEIEEE Access2169-35362019-01-01711776111776710.1109/ACCESS.2019.29363668805380NACOD: A Naïve Associative Classifier for Online DataYenny Villuendas-Rey0https://orcid.org/0000-0001-9889-3924Javier A. Hernandez-Castano1Oscar Camacho-Nieto2Cornelio Yanez-Marquez3Itzama Lopez-Yanez4Centro de Innovación y Desarrollo Tecnológico en Cómputo del Instituto Politécnico Nacional, Mexico City, MexicoCentro de Innovación y Desarrollo Tecnológico en Cómputo del Instituto Politécnico Nacional, Mexico City, MexicoCentro de Innovación y Desarrollo Tecnológico en Cómputo del Instituto Politécnico Nacional, Mexico City, MexicoCentro de Investigación en Computación del Instituto Politécnico Nacional, Mexico City, MexicoCentro de Innovación y Desarrollo Tecnológico en Cómputo del Instituto Politécnico Nacional, Mexico City, MexicoAnalyzing data in real time constitutes a challenge nowadays, due to the constant generation of data from different sources. To deal to such streams of data, in this paper we propose a novel decision-making algorithm within the associative approach. The proposed algorithm, named Naïve Associative Classifier for Online Data (NACOD), is able to deal with hybrid as well as with incomplete data. In addition, NACOD is transparent and transportable, which makes it a very useful decision-maker in environments that require such properties. The numerical experiments carried out show the effectiveness of NACOD.https://ieeexplore.ieee.org/document/8805380/Decision-makingonline learninghybrid and incomplete datanaïve associative classifier
spellingShingle Yenny Villuendas-Rey
Javier A. Hernandez-Castano
Oscar Camacho-Nieto
Cornelio Yanez-Marquez
Itzama Lopez-Yanez
NACOD: A Naïve Associative Classifier for Online Data
IEEE Access
Decision-making
online learning
hybrid and incomplete data
naïve associative classifier
title NACOD: A Naïve Associative Classifier for Online Data
title_full NACOD: A Naïve Associative Classifier for Online Data
title_fullStr NACOD: A Naïve Associative Classifier for Online Data
title_full_unstemmed NACOD: A Naïve Associative Classifier for Online Data
title_short NACOD: A Naïve Associative Classifier for Online Data
title_sort nacod a na x00ef ve associative classifier for online data
topic Decision-making
online learning
hybrid and incomplete data
naïve associative classifier
url https://ieeexplore.ieee.org/document/8805380/
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AT cornelioyanezmarquez nacodanax00efveassociativeclassifierforonlinedata
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