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Publication
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| Title of Article |
Probability-Weighted voting Ensemble Learning for Classification model |
| Date of Acceptance |
3 September 2020 |
| Journal |
| Title of Journal |
Journal of Advances in Information Technology |
| Standard |
SCOPUS |
| Institute of Journal |
Engineering and Technology Publishing |
| ISBN/ISSN |
1798-2340 |
| Volume |
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| Issue |
Volume 11, Issue 4 |
| Month |
November |
| Year of Publication |
2020 |
| Page |
217-227 |
| 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. |
| Keyword |
classification model, ensemble learning, machine learning, model combination, probability weight, weight voting |
| Author |
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| Reviewing Status |
มีผู้ประเมินอิสระ |
| Status |
ตีพิมพ์แล้ว |
| Level of Publication |
นานาชาติ |
| citation |
false |
| Part of thesis |
true |
| Attach file |
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| Citation |
0
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Journal Publication
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