A Review on Handling Imbalanced Data

Spelmen, Vimalraj S; Porkodi, R

doi:10.1109/icctct.2018.8551020

Cited by 88 publications

(47 citation statements)

References 31 publications

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“…(1) Predicting death from hospital-acquired infections in trauma patients in the absence of a balanced dataset (C5.0 and CHAID); (2) Predicting death from hospital-acquired infection in the trauma patients using a balanced dataset by sampling methods (reduced data set) (C5.0 and CHAID); (3) Clustering hospital-acquired infections in trauma patients by k-means algorithms; (4) Predicting death from hospital-acquired infections in trauma patients in each cluster (C5.0 and CHAID); (5) Predicting death from hospital-acquired infections in trauma patients with SMOTE-C5.0 and ADASYN-C5.0; (6) Predicting death from hospital-acquired infections in the trauma patients with SMOTE-SVM, ADASYN-SVM, SMOTE-ANN, and ADASYN-ANN. Many previous studies have attempted to handle unbalanced data [12][13][14] by adopting various approaches, such as using the right evaluation metrics, resampling the training set (under-sampling, and over-sampling), using K-fold cross-validation appropriately, ensemble different resampled datasets, resampling different ratios, and clustering the frequent class. However, no best model for these problems has been identified, while this strongly relates to techniques, models, and subjects used [2].…”

Section: Introductionmentioning

confidence: 99%

“…Many previous studies have attempted to handle unbalanced data [ 12 – 14 ] by adopting various approaches, such as using the right evaluation metrics, resampling the training set (under-sampling, and over-sampling), using K-fold cross-validation appropriately, ensemble different resampled datasets, resampling different ratios, and clustering the frequent class. However, no best model for these problems has been identified, while this strongly relates to techniques, models, and subjects used [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

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Mortality Prediction from Hospital-Acquired Infections in Trauma Patients Using an Unbalanced Dataset

et al. 2020

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mortality Prediction from Hospital-Acquired Infections in Trauma Patients Using an Unbalanced Dataset

et al. 2020

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“…DNN can be discriminatively trained with backpropagation that uses cost derivatives (f '(C)) to calculate the difference between the target output and actual output. Weights are updated using (7).…”

Section: Fig 2 Deep Neural Networkmentioning

confidence: 99%

“…The number of positive interactions is very small when compared to the negative interactions. This imbalance can cause inaccurate classification and prediction models [7]. Therefore, additional handling of imbalanced data is required.…”

Section: Introductionmentioning

confidence: 99%

Drug-Target Interaction Prediction in Coronavirus Disease 2019 Case Using Deep Semi-Supervised Learning Model

Sulistiawan

Kusuma

Ramadhanti

et al. 2020

2020 International Conference on Advanced Computer Science and Information Systems (ICACSIS)

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Coronavirus disease 2019 (COVID-19) is an infectious disease of the respiratory system that caused a pandemic in 2020. There is still not any effective special treatment to cure it. Drug repositioning is used to find an effective drug for curing new diseases by finding new efficacy of registered drug. The new efficacy can be conducted by elaborating the interactions between compounds and proteins (DTI). Deep Semi-Supervised Learning (DSSL) is used to overcome the lack of DTI information. DSSL utilizes unsupervised learning algorithms such as Stacked Auto Encoder (SAE) as pre-training for initializing weights on the Deep Neural Network (DNN). This study uses DSSL with a feature-based chemogenomics approach on the data resulted from the exploration of potential anti-coronavirus treatment. This study finds that the use of fingerprints for compound features and Dipeptide Composition (DC) for protein features gives the best results on accuracy (0.94), recall (0.83), precision (0.817), F-measure (0.822), and AUROC (0.97). From the test data predictions, 1766 and 929 positive interactions are found on the test data and herbal compounds, respectively. Keywords-coronavirus disease 2019, drug repositioning, deep semi-supervised learning, stacked autoencoder, deep neural network II. DEEP SEMI-SUPERVISED LEARNING FOR DTIResearch on drug repositioning is based on the fact that most drug compounds can activate or inhibit the biological functions of the target protein. This creates the needs to develop a DTI identification system [11]. DTI identification

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“…Generally, the classification of imbalanced datasets has significant attention in different fields thanks to its wide real applications (Spelmen and Porkodi 2018;Johnson and Khoshgoftaar 2019;López et al 2013). However, the imbalanced dataset problem has been neglected by most of the existing works about bankruptcy prediction.…”

Section: Introductionmentioning

confidence: 99%

Bankruptcy Prediction Using Deep Learning Approach Based on Borderline SMOTE

Smiti

Soui

2020

Inf Syst Front

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Imbalanced classification on bankruptcy prediction is considered as one of the most important topics in financial institutions. In this context, various statistical and artificial intelligence methods have been proposed. Recently, deep learning algorithms are experiencing a resurgence of interest, and are widely used to build a prediction and classification models. To this end, we propose a novel deep learning-based approach called BSM-SAES. This approach combines Borderline Synthetic Minority oversampling technique (BSM) and Stacked AutoEncoder (SAE) based on the Softmax classifier. The aim is to develop an accurate and reliable bankruptcy prediction model which includes the features extraction process. To assess the classification performance of our proposed model, k-nearest neighbor, decision tree, support vector machine, and artificial neural network, C5.0 that are machine learning methods, are applied. We evaluate our proposed approach on the Polish imbalanced datasets. The obtained results confirm the efficiency of our proposed model compared to other machine learning models regarding predicting and classifying the financial status of a firm.

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A Review on Handling Imbalanced Data

Cited by 88 publications

References 31 publications

Mortality Prediction from Hospital-Acquired Infections in Trauma Patients Using an Unbalanced Dataset

Mortality Prediction from Hospital-Acquired Infections in Trauma Patients Using an Unbalanced Dataset

Drug-Target Interaction Prediction in Coronavirus Disease 2019 Case Using Deep Semi-Supervised Learning Model

Bankruptcy Prediction Using Deep Learning Approach Based on Borderline SMOTE

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