Classifying DNA Methylation Imbalance Data in Cancer Risk Prediction Using SMOTE and Tomek Link Methods

Liu, Chao; Wu, Junbiao; Labrador, Mirador G.; Song, Yang; Hou, Weiyan

doi:10.1007/978-981-13-2206-8_1

Cited by 18 publications

(11 citation statements)

References 10 publications

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“…8 For example, effective prediction of common diseases and cancer risks by using SMOTE + TLTE for imbalanced medical data was observed in recent studies. 23,39 Similarly, here the drinking water quality prediction performance of all four traditional and three ensemble learning models was improved by training by the data set optimized by SMOTE + TLTE and SMOTE + ENNTE. One important reason is that SMOTE can form some new minority samples by interpolating between multiple minority samples, thereby radically increasing the proportion of minority samples.…”

Section: Discussionmentioning

confidence: 99%

Identification of Suitable Technologies for Drinking Water Quality Prediction: A Comparative Study of Traditional, Ensemble, Cost-Sensitive, Outlier Detection Learning Models and Sampling Algorithms

Chen

Liu

et al. 2021

ACS EST Water

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Drinking water quality data sets used in learning models have been highly imbalanced, which has weakened the prediction ability of models for drinking water quality. Although some efforts have been made to address the issue of imbalance, little is known about the suitable technologies for drinking water quality prediction. Here, a total of 16 common learning models were applied individually to compare the drinking water quality prediction performance based on a large-scale highly imbalanced drinking water quality data set. Our results showed that ensemble, cost-sensitive learning models with higher F1-scores were more suitable for predicting drinking water quality, compared to other models tested in this study. In addition, the learning model performance could be enhanced by the introduction of two mainstream sampling algorithms [synthetic minority oversampling technique (SMOTE) combined with the Tomek links technique (TLTE) or the edited nearest neighbor technique (ENNTE), SMOTE + TLTE or SMOTE + ENNTE, respectively]. In particular, the F1-scores of deep cascade forest (DCF) with SMOTE + TLTE or SMOTE + ENNTE reached 94.54 ± 2.51% and 94.68 ± 2.72%, respectively. As a consequence, DCF with these two sampling algorithms has greater potential to be applied in drinking water quality monitoring and prediction, as well as other fields that have suffered from issues of imbalanced data.

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Section: Discussionmentioning

confidence: 99%

Identification of Suitable Technologies for Drinking Water Quality Prediction: A Comparative Study of Traditional, Ensemble, Cost-Sensitive, Outlier Detection Learning Models and Sampling Algorithms

Chen

Liu

et al. 2021

ACS EST Water

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“…The previous studies showed that the performance of statistical models fitted with balanced data showed better results than the imbalanced data. When statistical models are fitted with imbalanced data, they are often biased towards the majority class and show poor performance in predicting the minority class [27][28][29]. Nevertheless, it is impossible to say how much imbalance in the distribution of the classes affects classification performance because other elements like sample size, relevance of predictor variables, etc.…”

Section: Imbalance Datamentioning

confidence: 99%

Developing machine learning-based models to predict intrauterine insemination (IUI) success by address modeling challenges in imbalanced data and providing modification solutions for them

Khodabandelu

Basirat

Khaleghi

et al. 2022

BMC Med Inform Decis Mak

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Background This study sought to provide machine learning-based classification models to predict the success of intrauterine insemination (IUI) therapy. Additionally, we sought to illustrate the effect of models fitting with balanced data vs original data with imbalanced data labels using two different types of resampling methods. Finally, we fit models with all features against optimized feature sets using various feature selection techniques. Methods The data for the cross-sectional study were collected from 546 infertile couples with IUI at the Fatemehzahra Infertility Research Center, Babol, North of Iran. Logistic regression (LR), support vector classification, random forest, Extreme Gradient Boosting (XGBoost) and, Stacking generalization (Stack) as the machine learning classifiers were used to predict IUI success by Python v3.7. We employed the Smote-Tomek (Stomek) and Smote-ENN (SENN) resampling methods to address the imbalance problem in the original dataset. Furthermore, to increase the performance of the models, mutual information classification (MIC-FS), genetic algorithm (GA-FS), and random forest (RF-FS) were used to select the ideal feature sets for model development. Results In this study, 28% of patients undergoing IUI treatment obtained a successful pregnancy. Also, the average age of women and men was 24.98 and 29.85 years, respectively. The calibration plot in this study for IUI success prediction by machine learning models showed that between feature selection methods, the RF-FS, and among the datasets used to fit the models, the balanced dataset with the Stomek method had well-calibrating predictions than other methods. Finally, the brier scores for the LR, SVC, RF, XGBoost, and Stack models that were fitted utilizing the Stomek dataset and the chosen feature set using the Random Forest technique obtained equal to 0.202, 0.183, 0.158, 0.129, and 0.134, respectively. It showed duration of infertility, male and female age, sperm concentration, and sperm motility grading score as the most predictable factors in IUI success. Conclusion The results of this study with the XGBoost prediction model can be used to foretell the individual success of IUI for each couple before initiating therapy.

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“…Kamarulzalis et al [14] apply SMOTE to gender analysis, in which J48 is used as the classifier. Liu et al [15] apply SMOTE-TL to cancer risk prediction. Nakamura et al [16] propose a novel SMOTEbased method using codebooks obtained by the learning vector quantization, and then apply the proposed SMOTE in biomedical data.…”

Section: Related Workmentioning

confidence: 99%

“…Hybrid techniques, such as S-SulfPred [12] and SSOMaj-SMOTE-SSOMin [13], are developed and combine oversampling techniques with undersampling techniques. Among oversampling techniques, the Synthetic Minority Over-sampling Technique (SMOTE) [5] is the most successful due to a lot of admiration and extensive practice, such as gender analysis [14], bioengineering [15], medical examination [16], Fraud identification [17].…”

Section: Introductionmentioning

confidence: 99%

Synthetic Minority Oversampling Technique Based on Adaptive Local Mean Vectors and Improved Differential Evolution

Zhang¹,

2022

IEEE Access

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SMOTE is a classical oversampling method and aims to improve imbalanced classification by creating synthetic minority class samples. Overgeneralization is a great challenge in SMOTE and its improvements. Multiple variations of SMOTE are proposed against imbalances between classes and overgeneralization. However, they still have the following issues: a) most methods depend on too many parameters; b) most methods fail to detect suspicious noise effectively and modify them; c) interpolation of almost all methods is susceptible to abnormal samples. To overcome the above issues, a new synthetic minority oversampling technique based on adaptive local mean vectors and improved differential evolution (SMOTE-LMVDE) is proposed. First, a new noise detection technique based on the defined adaptive local mean vectors (NDALMV) is proposed to find suspicious noise. Second, an improved differential evolution is proposed to modify and improve detected suspicious noise. Finally, a new interpolation based on the defined adaptive local mean vectors is proposed to create synthetic minority class samples. Experiments prove that the proposed method superior to 7 popular oversampling approaches on extensive data sets in the training nearest neighbor classifier and the decision tree classifier.

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Classifying DNA Methylation Imbalance Data in Cancer Risk Prediction Using SMOTE and Tomek Link Methods

Cited by 18 publications

References 10 publications

Identification of Suitable Technologies for Drinking Water Quality Prediction: A Comparative Study of Traditional, Ensemble, Cost-Sensitive, Outlier Detection Learning Models and Sampling Algorithms

Identification of Suitable Technologies for Drinking Water Quality Prediction: A Comparative Study of Traditional, Ensemble, Cost-Sensitive, Outlier Detection Learning Models and Sampling Algorithms

Developing machine learning-based models to predict intrauterine insemination (IUI) success by address modeling challenges in imbalanced data and providing modification solutions for them

Synthetic Minority Oversampling Technique Based on Adaptive Local Mean Vectors and Improved Differential Evolution

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