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General Information
ISSN:
1798-2340 (Online)
Frequency:
Monthly
DOI:
10.12720/jait
Indexing:
ESCI (Web of Science)
,
Scopus
,
CNKI
,
etc
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Acceptance Rate:
19%
APC:
500 USD
Average Days to Accept:
135 days
Journal Metrics:
Impact Factor 2022: 1.0
3.1
2022
CiteScore
49th percentile
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Editor-in-Chief
Prof. Kin C. Yow
University of Regina, Saskatchewan, Canada
I'm delighted to serve as the Editor-in-Chief of
Journal of Advances in Information Technology
.
JAIT
is intended to reflect new directions of research and report latest advances in information technology. I will do my best to increase the prestige of the journal.
What's New
2024-03-28
Vol. 15, No. 3 has been published online!
2024-02-26
The papers published in Vol. 15, Nos. 1&2 have been registered with Crossref.
2024-02-26
Vol. 15, No. 2 has been published online!
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2020
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Volume 11, No. 4, November 2020
>
Probability-Weighted Voting Ensemble Learning for Classification Model
Artitayapron Rojarath and Wararat Songpan
Department of Computer Science Faculty of Science, Khon Kaen University, Thailand
Abstract
—Many research studies have investigated ensemble learning. However, these research studies proposed an approach for improving the ensemble learning. We propose the efficiency method using probability weight as a support to the classifier model called the probability-weighted voting ensemble learning, which computes its own probability computation for each model from the training data. This research has tested the proposed model with 5 UCI data sets in various dimensions and generated four models, the 3PW-Ensemble model, the 4PW-Ensemble model, the 5PW-Ensemble model, and the 6PW-Ensemble model. The experimental results of the study yield the highest accuracy. Considering the comparison of efficiency, the accuracy of the proposed model was higher than those of the based classification models and the other ensemble models.
Index Terms
—classification model, ensemble learning, machine learning, model combination, probability weight, weight voting
Cite: Artitayapron Rojarath and Wararat Songpan, "Probability-Weighted Voting Ensemble Learning for Classification Model," Journal of Advances in Information Technology, Vol. 11, No. 4, pp. 217-227, November 2020. doi: 10.12720/jait.11.4.217-227
Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License (
CC BY-NC-ND 4.0
), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.
5-L0344_Thailand
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