Performance of SMOTE in a random forest and naive Bayes classifier for imbalanced Hepatitis-B vaccination status

Putri, V M; Masjkur, Mohammad; Suhaeni, Cici

doi:10.1088/1742-6596/1863/1/012073

Cited by 7 publications

(2 citation statements)

References 6 publications

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“…Random forest (randomForest package) was used to identify microbiome taxa predictive of OA and KL [52] groups. We generated a "SMOTE" (Synthetic Minority Oversampling Technique) (consisting of 280 OA and 336 KL) dataset using the package DMwR [58] to address the imbalance number of samples between OA (n = 594) and KL (n = 56) samples [59]. SMOTE algorithm is a technique to address the imbalanced datasets by oversampling the minority class.…”

Section: Discussionmentioning

confidence: 99%

Gut microbiome of helminth-infected indigenous Malaysians is context dependent

Tee

Easton

et al. 2022

Microbiome

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Background While microbiomes in industrialized societies are well characterized, indigenous populations with traditional lifestyles have microbiomes that are more akin to those of ancient humans. However, metagenomic data in these populations remains scarce, and the association with soil-transmitted helminth infection status is unclear. Here, we sequenced 650 metagenomes of indigenous Malaysians from five villages with different prevalence of helminth infections. Results Individuals from villages with higher prevalences of helminth infections have more unmapped reads and greater microbial diversity. Microbial community diversity and composition were most strongly associated with different villages and the effects of helminth infection status on the microbiome varies by village. Longitudinal changes in the microbiome in response to albendazole anthelmintic treatment were observed in both helminth infected and uninfected individuals. Inference of bacterial population replication rates from origin of replication analysis identified specific replicating taxa associated with helminth infection. Conclusions Our results indicate that helminth effects on the microbiota were highly dependent on context, and effects of albendazole on the microbiota can be confounding for the interpretation of deworming studies. Furthermore, a substantial quantity of the microbiome remains unannotated, and this large dataset from an indigenous population associated with helminth infections is a valuable resource for future studies.

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Section: Discussionmentioning

confidence: 99%

Gut microbiome of helminth-infected indigenous Malaysians is context dependent

Tee

Easton

et al. 2022

Microbiome

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“…The random forest algorithm makes a decision tree prediction more efficient and increases the accuracy and the robustness of the classification model (Bisht et al, 2016;Putri et al, 2021).…”

Section: Methodsmentioning

confidence: 99%

Combining SMOTE and OVA with Deep Learning and Ensemble Classifiers for Multiclass Imbalanced

Panthong¹

2022

Journal of Computer Science

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The classification of real-world problems always consists of imbalanced and multiclass datasets. A dataset having unbalanced and multiple classes will have an impact on the pattern of the classification model and the classification accuracy, which will be decreased. Hence, oversampling method keeps the class of dataset balanced and avoids the overfitting problem. The purposes of the study were to handle multiclass imbalanced datasets and to improve the effectiveness of the classification model. This study proposed a hybrid method by combining the Synthetic Minority Oversampling Technique (SMOTE) and One-Versus-All (OVA) with deep learning and ensemble classifiers; stacking and random forest algorithms for multiclass imbalanced data handling. Datasets consisting of different numbers of classes and imbalances are gained from the UCI Machine Learning Repository. The research outputs illustrated that the presented method acquired the best accuracy value at 98.51% when the deep learning classifier was used to evaluate model classification performance in the new-thyroid dataset. The proposed method using the stacking algorithm received a higher accuracy rate than other methods in the car, pageblocks, and Ecoli datasets. In addition, the outputs gained the highest performance of classification at 98.47% in the dermatology dataset where the random forest is used as a classifier.

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