Improving Machine Learning Prediction of Peatlands Fire Occurrence for Unbalanced Data Using SMOTE Approach

Rosadi, Dedi; Arisanty, Deasy; Andriyani, Widyastuti; Peiris, Shelton; Agustina, Dina; Dowe, David L.; Fang, Zhiwei

doi:10.1109/databia53375.2021.9650084

Cited by 15 publications

(3 citation statements)

References 7 publications

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“…Our study found that the age variable emerged as the most significant factor in our SMOTE_RF analysis, with a notably high turnover probability observed among younger nurses. This observation is in alignment with the findings of several previous studies [6], [8], [21], [30], all of which have highlighted age as a major determinant influencing nurse turnover. The inclination for younger nurses to exhibit higher turnover rates can be attributed to various factors.…”

Section: Discussionsupporting

confidence: 92%

Predicting Nurse Turnover for Highly Imbalanced Data Using SMOTE and Machine Learning Algorithms

Xu,

Park,

Park

et al. 2023

Preprint

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Predicting nurse turnover is a growing challenge within the healthcare sector, profoundly impacting healthcare quality and the nursing profession. This study employs the Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance issues in the 2018 National Sample Survey of Registered Nurses (NSSRN) dataset and predict nurse turnover using machine learning (ML) algorithms. Four ML algorithms, namely logistic regression (LR), random forests (RF), decision tree (DT), and extreme gradient boosting (XGBoost), are applied to the SMOTE-enhanced dataset. The data is randomly split into an 80% training set and a 20% validation set. Eighteen carefully selected variables from the NSSRN database serve as predictive features, and the machine learning model identifies feature importance concerning nurse turnover. The study includes a performance comparison based on metrics such as Accuracy, Precision, Recall (Sensitivity), F1-score, and AUC. In summary, the results demonstrate that SMOTE-enhanced random forests (SMOTE_RT) exhibit the most robust predictive power, both in the classical approach (with all 18 predictive variables) and an optimized approach (utilizing eight key predictive variables). XGBoost, decision tree, and logistic regression follow in performance. Notably, age emerges as the most influential factor in nurse turnover, with working hours, EHR/EMR usability, individual income, and region also playing significant roles. This research offers valuable insights for healthcare researchers and stakeholders, aiding in selecting suitable ML algorithms for nurse turnover prediction.

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Section: Discussionsupporting

confidence: 92%

Predicting Nurse Turnover for Highly Imbalanced Data Using SMOTE and Machine Learning Algorithms

Xu,

Park,

Park

et al. 2023

Preprint

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“…Both have their own sets of benefits and drawbacks. This research uses the Synthetic Minority Oversampling Technique (SMOTE) to handle the imbalanced data [66]. It duplicates the minority class instances by using an existing instance to make new instances.…”

Section: K Data Samplingmentioning

confidence: 99%

Malicious Node Detection Using Machine Learning and Distributed Data Storage Using Blockchain in WSNs

et al. 2023

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In the proposed work, blockchain is implemented on the Base Stations (BSs) and Cluster Heads (CHs) to register the nodes using their credentials and also to tackle various security issues. Moreover, a Machine Learning (ML) classifier, termed as Histogram Gradient Boost (HGB), is employed on the BSs to classify the nodes as malicious or legitimate. In case, the node is found to be malicious, its registration is revoked from the network. Whereas, if a node is found to be legitimate, then its data is stored in an Interplanetary File System (IPFS). IPFS stores the data in the form of chunks and generates hash for the data, which is then stored in blockchain. In addition, Verifiable Byzantine Fault Tolerance (VBFT) is used instead of Proof of Work (PoW) to perform consensus and validate transactions. Also, extensive simulations are performed using the Wireless Sensor Network (WSN) dataset, referred as WSN-DS. The proposed model is evaluated both on the original dataset and the balanced dataset. Furthermore, HGB is compared with other existing classifiers, Adaptive Boost (AdaBoost), Gradient Boost (GB), Linear Discriminant Analysis (LDA), Extreme Gradient Boost (XGB) and ridge, using different performance metrics like accuracy, precision, recall, micro-F1 score and macro-F1 score. The performance evaluation of HGB shows that it outperforms GB, AdaBoost, LDA, XGB and Ridge by 2-4%, 8-10%, 12-14%, 3-5% and 14-16%, respectively. Moreover, the results with balanced dataset are better than those with original dataset. Also, VBFT performs 20-30% better than PoW. Overall, the proposed model performs efficiently in terms of malicious node detection and secure data storage.

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“…In engineering practice, this kind of misjudg-ment of abnormal line-to-line relationship will cause serious consequences. Therefore, when classifying the linear relationship, it is necessary to consider the limitations of traditional classifier algorithms on unbalanced data sets and adopt appropriate unbalanced data processing methods to improve the classification effect of traditional classification algorithms [16][17][18]. This article deals with imbalanced data from the perspective of data augmentation.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Identification of the Line-Transformer Relationship in Distribution Networks Based on GAN Processing Unbalanced Data

et al. 2022

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The wrong line-transformer relationship is one of the main reasons that leads to the failure of the line loss assessment of the distribution network with voltage levels of 10 kV and below. The traditional manual method to verify the line-transformer relationship is time-consuming, labor-intensive and inefficient. At the same time, due to the small sample size of the data with abnormal line-transformer relationship, the unbalanced sample data reduces the accuracy of the artificial intelligence algorithm. To this end, this paper proposes an intelligent identification method for distribution network line-transformer relationship based on Generative Adversarial Networks (GAN) processing unbalanced data. Firstly, perform data preprocessing and feature extraction based on the input power of the distribution line and the power consumption of each distribution transformer; then, build a GAN-based model for expanding the data of only a small number of abnormal line-transformer relationship samples, so as to solve the problem of unbalanced sample data distribution; and finally, establish a support vector machine (SVM) to realize the classification of the line-transformer relationship. The results of the example simulation show that, compared with the traditional Synthetic Minority Oversampling Technique (SMOTE) for processing unbalanced data, the classification effect of the proposed GAN-based data augmentation method has been significantly improved. In addition, the recall rate of the three types of the line-transformer relationship (line hanging error, magnification error and normal) under the line-transformer relationship identification method proposed in this paper is more than 92%, which proves the effectiveness and feasibility of the method.

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Improving Machine Learning Prediction of Peatlands Fire Occurrence for Unbalanced Data Using SMOTE Approach

Cited by 15 publications

References 7 publications

Predicting Nurse Turnover for Highly Imbalanced Data Using SMOTE and Machine Learning Algorithms

Predicting Nurse Turnover for Highly Imbalanced Data Using SMOTE and Machine Learning Algorithms

Malicious Node Detection Using Machine Learning and Distributed Data Storage Using Blockchain in WSNs

Intelligent Identification of the Line-Transformer Relationship in Distribution Networks Based on GAN Processing Unbalanced Data

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