Near-Bayesian Support Vector Machines for imbalanced data classification with equal or unequal misclassification costs

Datta, Siddhartha; Das, Swagatam

doi:10.1016/j.neunet.2015.06.005

Cited by 153 publications

(55 citation statements)

References 39 publications

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“…In the case of class‐imbalanced populations, the probability of an unknown case will be biased towards the majority class. Similarly, the popular classifier of support vector machines performs poorly with imbalanced populations. ANNs adjust their weights based on a classification error, as explained above.…”

Section: Methodsmentioning

confidence: 99%

Two‐stage approach for risk estimation of fetal trisomy 21 and other aneuploidies using computational intelligence systems

Neocleous

Syngelaki

Nicolaides

et al. 2018

Ultrasound in Obstet & Gyne

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We propose a method for early diagnosis of chromosomal abnormalities that ensures that most T21 cases are classified as high risk at any stage. At the same time, the number of euploid cases subjected to invasive or cell-free DNA examinations was minimized through a routine procedure offered in two stages. Our method is minimally invasive and of relatively low cost, highly effective at T21 identification and it performs better than do other existing statistical methods. Copyright © 2017 ISUOG. Published by John Wiley & Sons Ltd.

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Section: Methodsmentioning

confidence: 99%

Two‐stage approach for risk estimation of fetal trisomy 21 and other aneuploidies using computational intelligence systems

Neocleous

Syngelaki

Nicolaides

et al. 2018

Ultrasound in Obstet & Gyne

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“…Machine learning approaches are likely to perform poorly in situations with data imbalance between the classes. 21 , 22 In order to balance the dataset, we generated a decoy dataset (249 compounds) using the DUD-E online automated tool. 23 Finally, a dataset with 356 active compounds and 356 inactive compounds was obtained.…”

Section: Methodsmentioning

confidence: 99%

Prediction of selective estrogen receptor beta agonist using open data and machine learning approach

Niu¹,

Xie²,

Wang³

et al. 2016

DDDT

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BackgroundEstrogen receptors (ERs) are nuclear transcription factors that are involved in the regulation of many complex physiological processes in humans. ERs have been validated as important drug targets for the treatment of various diseases, including breast cancer, ovarian cancer, osteoporosis, and cardiovascular disease. ERs have two subtypes, ER-α and ER-β. Emerging data suggest that the development of subtype-selective ligands that specifically target ER-β could be a more optimal approach to elicit beneficial estrogen-like activities and reduce side effects.MethodsHerein, we focused on ER-β and developed its in silico quantitative structure-activity relationship models using machine learning (ML) methods.ResultsThe chemical structures and ER-β bioactivity data were extracted from public chemogenomics databases. Four types of popular fingerprint generation methods including MACCS fingerprint, PubChem fingerprint, 2D atom pairs, and Chemistry Development Kit extended fingerprint were used as descriptors. Four ML methods including Naïve Bayesian classifier, k-nearest neighbor, random forest, and support vector machine were used to train the models. The range of classification accuracies was 77.10% to 88.34%, and the range of area under the ROC (receiver operating characteristic) curve values was 0.8151 to 0.9475, evaluated by the 5-fold cross-validation. Comparison analysis suggests that both the random forest and the support vector machine are superior for the classification of selective ER-β agonists. Chemistry Development Kit extended fingerprints and MACCS fingerprint performed better in structural representation between active and inactive agonists.ConclusionThese results demonstrate that combining the fingerprint and ML approaches leads to robust ER-β agonist prediction models, which are potentially applicable to the identification of selective ER-β agonists.

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“…These include the cost-sensitive learners, like cost-sensitive kNN [12], costsensitive C4.5 [11], cost-sensitive SVM [36,37] and cost-sensitive neural networks [38], which modify traditional classifiers by assigning different costs to the misclassification of minority and majority instances. These costs are used in the construction of the classification model and reduce the dominance of majority over minority elements.…”

Section: Imbalanced Classificationmentioning

confidence: 99%

EPRENNID: An evolutionary prototype reduction based ensemble for nearest neighbor classification of imbalanced data

et al. 2016

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Classification problems with an imbalanced class distribution have received an increased amount of attention within the machine learning community over the last decade. They are encountered in a growing number of realworld situations and pose a challenge to standard machine learning techniques. We propose a new hybrid method specifically tailored to handle class imbalance, called EPRENNID. It performs an evolutionary prototype reduction focused on providing diverse solutions to prevent the method from overfitting the training set. It also allows us to explicitly reduce the underrepresented class, which the most common preprocessing solutions handling class imbalance usually protect. As part of the experimental study, we show that the proposed prototype reduction method outperforms state-of-the-art preprocessing techniques. The preprocessing step yields multiple prototype sets that are later used in an ensemble, performing a weighted voting scheme with the nearest neighbor classifier. EPRENNID is experimentally shown to significantly outperform previous proposals.

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Near-Bayesian Support Vector Machines for imbalanced data classification with equal or unequal misclassification costs

Cited by 153 publications

References 39 publications

Two‐stage approach for risk estimation of fetal trisomy 21 and other aneuploidies using computational intelligence systems

Two‐stage approach for risk estimation of fetal trisomy 21 and other aneuploidies using computational intelligence systems

Prediction of selective estrogen receptor beta agonist using open data and machine learning approach

EPRENNID: An evolutionary prototype reduction based ensemble for nearest neighbor classification of imbalanced data

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