Solving class imbalance problem using bagging, boosting techniques, with and without using noise filtering method

Rekha, Gillala; Tyagi, Amit Kumar; Reddy, V. Krishna

doi:10.3233/his-190261

Cited by 12 publications

(6 citation statements)

References 31 publications

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“…Class imbalance can potentially skew the performance of predictive models, as machine learning algorithms tend to favour the majority class. To address this issue, we carefully selected machine learning algorithms such as random forest and LightGBM, which excel in handling imbalanced datasets [ 41 , 42 ] and experimented with ensemble learning techniques, such as bagging and boosting, to enhance our model's overall performance [ 43 , 44 ]. Additionally, it is crucial to note that the actual distribution of patients in ED is inherently imbalanced, and our dataset truly reflects this patient distribution.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning-Based Models for Prediction of Critical Illness at Community, Paramedic, and Hospital Stages

Lee

Park

Hwang

et al. 2023

Emergency Medicine International

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Overcrowding of emergency department (ED) has put a strain on national healthcare systems and adversely affected the clinical outcomes of critically ill patients. Early identification of critically ill patients prior to ED visits can help induce optimal patient flow and allocate medical resources effectively. This study aims to develop ML-based models for predicting critical illness in the community, paramedic, and hospital stages using Korean National Emergency Department Information System (NEDIS) data. Random forest and light gradient boosting machine (LightGBM) were applied to develop predictive models. The predictive model performance based on AUROC in community stage, paramedic stage, and hospital stage was estimated to be 0.870 (95% CI: 0.869–0.871), 0.897 (95% CI: 0.896–0.898), and 0.950 (95% CI: 0.949–0.950) in random forest and 0.877 (95% CI: 0.876–0.878), 0.899 (95% CI: 0.898–0.900), and 0.950 (95% CI: 0.950–0.951) in LightGBM, respectively. The ML models showed high performance in predicting critical illness using variables available at each stage, which can be helpful in guiding patients to appropriate hospitals according to their severity of illness. Furthermore, a simulation model can be developed for proper allocation of limited medical resources.

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Section: Discussionmentioning

confidence: 99%

Machine Learning-Based Models for Prediction of Critical Illness at Community, Paramedic, and Hospital Stages

Lee

Park

Hwang

et al. 2023

Emergency Medicine International

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“…A lot of research papers [2,12,13] have create a comprehensive examination of imbalanced datasets. These studies have not only conducted reviews but have also put forth various solutions aimed at effectively addressing the challenge of imbalanced data.…”

Section: Related Workmentioning

confidence: 99%

HHO-SMOTe: Efficient Sampling Rate for Synthetic Minority Oversampling Technique Based on Harris Hawk Optimization

Raslan,

Alsharkawy,

Raslan

2023

IJACSA

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Classifying imbalanced datasets presents a significant challenge in the field of machine learning, especially with big data, where instances are unevenly distributed among classes, leading to class imbalance issues that affect classifier performance. Synthetic Minority Over-sampling Technique (SMOTE) is an effective oversampling method that addresses this by generating new instances for the under-represented minority class. However, SMOTE's efficiency relies on the sampling rate for minority class instances, making optimal sampling rates crucial for solving class imbalance. In this paper, we introduce HHO-SMOTe, a novel hybrid approach that combines the Harris Hawk optimization (HHO) search algorithm with SMOTE to enhance classification accuracy by determining optimal sample rates for each dataset. We conducted extensive experiments across diverse datasets to comprehensively evaluate our binary classification model. The results demonstrated our model's exceptional performance, with an AUC score exceeding 0.96, a high G-means score of 0.95 highlighting its robustness, and an outstanding F1-score consistently exceeding 0.99. These findings collectively establish our proposed approach as a formidable contender in the domain of binary classification models.

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“…Most of these approaches/techniques applied machine learning algorithms to predict churn. As data is imbalance in nature, many techniques were proposed in the literature [3][4][5]. Gavril et al [6] presented machine learning technique to predict the prepaid customers churn using 3333 customers data.…”

Section: Literature Surveymentioning

confidence: 99%

Performance Analysis of Undersampling Approaches for Solving Customer Churn Prediction

Ambildhuke

Rekha

Tyagi

2021

Lecture Notes in Networks and Systems

Self Cite

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With the increasing growth of technologies and digitization in telecom industries, retention of customers has been a serious concern. The prediction of customer retention/customer churn is a challenging task for telecom industry. This problem greatly affects the revenue of telecommunication industry. A good customer churn prediction model is needed to reduce the revenue loss and to rapidly increase the business. However, the development of good model is a challenging task due to the imbalanced nature of data, large size of data, high-dimensional features, and many more. In this paper, we focus on imbalanced data distribution by presenting a solution in balancing it in a very effective way. We employed undersampling techniques using decision tree and with boosting. Thus, this paper outlines the various undersampling techniques using single and ensemble classifiers in solving the customer churn prediction problem.

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Solving class imbalance problem using bagging, boosting techniques, with and without using noise filtering method

Cited by 12 publications

References 31 publications

Machine Learning-Based Models for Prediction of Critical Illness at Community, Paramedic, and Hospital Stages

Machine Learning-Based Models for Prediction of Critical Illness at Community, Paramedic, and Hospital Stages

HHO-SMOTe: Efficient Sampling Rate for Synthetic Minority Oversampling Technique Based on Harris Hawk Optimization

Performance Analysis of Undersampling Approaches for Solving Customer Churn Prediction

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