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COMPARISON OF RACOG AND RACOG-RUS FOR CLASSIFYING IMBALANCED DATA ON GRADIENT BOOSTING AND NAÏVE BAYES PERFORMANCE

Rahmi Fadhilah et al.

Journal of Modern Applied Statistical Methods2024https://doi.org/10.56801/jmasm.v24.i1.6article
ABDC B
Weight
0.30

Abstract

This study aims to determine the effect of resampling RACOG and RACOG-RUS data on Gradient Boosting and Naïve Bayes classification in predicting water quality with unbalanced data. The data used in this study were 720 data from January 2022 to December 2023. It was found that Gradient Boosting performed best when using RACOG-RUS resampling data and feature selection with a number of numIntances of 200. While Naïve Bayes has the best performance when using RACOG-RUS resampling data without feature selection with a number of numIntances of 300. It can be seen that resampling RACOG data does not outperform RACOG-RUS in both classification models because it is known that the data generated in RACOG does not make the dataset more balanced than RACOG-RUS. Hybrid sampling is necessary if RACOG samples are used as the training dataset.

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https://doi.org/https://doi.org/10.56801/jmasm.v24.i1.6

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@article{rahmi2024,
  title        = {{COMPARISON OF RACOG AND RACOG-RUS FOR CLASSIFYING IMBALANCED DATA ON GRADIENT BOOSTING AND NAÏVE BAYES PERFORMANCE}},
  author       = {Rahmi Fadhilah et al.},
  journal      = {Journal of Modern Applied Statistical Methods},
  year         = {2024},
  doi          = {https://doi.org/https://doi.org/10.56801/jmasm.v24.i1.6},
}

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COMPARISON OF RACOG AND RACOG-RUS FOR CLASSIFYING IMBALANCED DATA ON GRADIENT BOOSTING AND NAÏVE BAYES PERFORMANCE

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Evidence weight

0.30

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.00 × 0.4 = 0.00
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.