SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary

Fernández, Alberto; García, Salvador; Herrera, Francisco; Chawla, Nitesh V.

doi:10.1613/jair.1.11192

Cited by 1,383 publications

(673 citation statements)

References 214 publications

Supporting

Mentioning

657

Contrasting

Unclassified

Order By: Relevance

“…(c) How to select suitable attributes and determine their weights is another problem awaiting resolution. (d) The empirical studies of the three‐way decision module could be conducted, and some approaches of dealing with imbalanced classification may be utilized (Branco, Torgo, & Ribeiro, ; Fernández, Garcia, Herrera, & Chawla, ; Torgo, Ribeiro, Pfahringer, & Branco, ). The abovementioned future works can help to enrich our knowledge of applying advanced researches to practical problems.…”

Section: Resultsmentioning

confidence: 99%

A multiperiod hybrid decision support model for medical diagnosis and treatment based on similarities and three‐way decision theory

Yan

Liu

et al. 2019

Expert Systems

View full text Add to dashboard Cite

Patients always want a precise diagnosis and appropriate treatment advice when they are diagnosed with a disease. Furthermore, the original information about certain features and symptoms appears in different forms, and the effect of time is always ignored in the process of diagnosis. To overcome these defects, this paper develops a systematic multiperiod hybrid decision support model, which combines the similarity measurement and three‐way decision theory to provide prediction and treatment advice for patients under the fuzzy environment. This multiperiod hybrid decision support model, which considers the effect of time, including transformation module, multiperiod integration module, similarity module, prediction module, and three‐way decision module, provides disease prediction and advice on treatment based on similarities and three‐way decision theory. To validate this model, we construct an illustration composed of four cases, and this ultimately shows that MPH‐SDM can effectively support patients' disease diagnoses and treatment.

show abstract

Section: Resultsmentioning

confidence: 99%

A multiperiod hybrid decision support model for medical diagnosis and treatment based on similarities and three‐way decision theory

Yan

Liu

et al. 2019

Expert Systems

View full text Add to dashboard Cite

show abstract

“…• Now, in the dataset, the number of data points for one class is very less when compared to the other class corresponding to the COVID-19 patients. Therefore, to balance the imbalanced data points, we use synthetic minority oversampling technique (SMOTE) 23,24 . This algorithm creates an equal number of samples for each class.…”

Section: /10mentioning

confidence: 99%

Accurate Prediction of COVID-19 using Chest X-Ray Images through Deep Feature Learning model with SMOTE and Machine Learning Classifiers

Kumar

Arora

Bansal

et al. 2020

Preprint

116

View full text Add to dashboard Cite

According to the World Health Organization (WHO), the coronavirus (COVID-19) pandemic is putting even the best healthcare systems across the world under tremendous pressure. The early detection of this type of virus will help in relieving the pressure of the healthcare systems. Chest X-rays has been playing a crucial role in the diagnosis of diseases like Pneumonia. As COVID-19 is a type of influenza, it is possible to diagnose using this imaging technique. With rapid development in the area of Machine Learning (ML) and Deep learning, there had been intelligent systems to classify between Pneumonia and Normal patients. This paper proposes the machine learning-based classification of the extracted deep feature using ResNet152 with COVID-19 and Pneumonia patients on chest X-ray images. SMOTE is used for balancing the imbalanced data points of COVID-19 and Normal patients. This non-invasive and early prediction of novel coronavirus (COVID-19) by analyzing chest X-rays can further be used to predict the spread of the virus in asymptomatic patients. The model is achieving an accuracy of 0.973 on Random Forest and 0.977 using XGBoost predictive classifiers. The establishment of such an approach will be useful to predict the outbreak early, which in turn can aid to control it effectively.

show abstract

“…It generates artificial samples from the minority class by interpolating existing instances that lie close together. Nowadays, it is one the most popular data sampling methods [1] and it has motivated the development of other over-samplings algorithms [24]. Similarly, under-sampling methods have been incorporate a heuristic component [25], some of the most outstanding examples being the Tomek's Links (TL) [26], Editing Nearest Neighbor (ENN) [27], and Condensed Nearest Neighbor rule (CNN) [28], among others [22,[29][30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

“…It has seen that big data class imbalance approaches have been addressed by adaptation of traditional techniques, mainly sampling methods [21,44]. However, recent studies show that some conclusions from machine learning are not applicable to the big data context; for example, in machine learning is common that SMOTE performs better than ROS [24], but in big data some results do not show this trend [1,48]. In addition, only a few works have been addressed to deal with the class imbalance in big data by using "intelligent" or heuristic sampling techniques [17,49].…”

Section: Introductionmentioning

confidence: 99%

Data Sampling Methods to Deal With the Big Data Multi-Class Imbalance Problem

et al. 2020

View full text Add to dashboard Cite

The class imbalance problem has been a hot topic in the machine learning community in recent years. Nowadays, in the time of big data and deep learning, this problem remains in force. Much work has been performed to deal to the class imbalance problem, the random sampling methods (over and under sampling) being the most widely employed approaches. Moreover, sophisticated sampling methods have been developed, including the Synthetic Minority Over-sampling Technique (SMOTE), and also they have been combined with cleaning techniques such as Editing Nearest Neighbor or Tomek's Links (SMOTE+ENN and SMOTE+TL, respectively). In the big data context, it is noticeable that the class imbalance problem has been addressed by adaptation of traditional techniques, relatively ignoring intelligent approaches. Thus, the capabilities and possibilities of heuristic sampling methods on deep learning neural networks in big data domain are analyzed in this work, and the cleaning strategies are particularly analyzed. This study is developed on big data, multi-class imbalanced datasets obtained from hyper-spectral remote sensing images. The effectiveness of a hybrid approach on these datasets is analyzed, in which the dataset is cleaned by SMOTE followed by the training of an Artificial Neural Network (ANN) with those data, while the neural network output noise is processed with ENN to eliminate output noise; after that, the ANN is trained again with the resultant dataset. Obtained results suggest that best classification outcome is achieved when the cleaning strategies are applied on an ANN output instead of input feature space only. Consequently, the need to consider the classifier's nature when the classical class imbalance approaches are adapted in deep learning and big data scenarios is clear.

show abstract

SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary

Cited by 1,383 publications

References 214 publications

A multiperiod hybrid decision support model for medical diagnosis and treatment based on similarities and three‐way decision theory

A multiperiod hybrid decision support model for medical diagnosis and treatment based on similarities and three‐way decision theory

Accurate Prediction of COVID-19 using Chest X-Ray Images through Deep Feature Learning model with SMOTE and Machine Learning Classifiers

Data Sampling Methods to Deal With the Big Data Multi-Class Imbalance Problem

Contact Info

Product

Resources

About