Effective data generation for imbalanced learning using conditional generative adversarial networks

Douzas, Georgios; Bação, Fernando

doi:10.1016/j.eswa.2017.09.030

Cited by 429 publications

(199 citation statements)

References 24 publications

Supporting

Mentioning

196

Contrasting

Unclassified

Order By: Relevance

“…Such approaches generally use convolutional neural networks (CNNs) [25][26][27] or conditional generative adversarial networks (cGANs) [28][29][30], where CNNs are based on the application of a single NN, and cGANS are based on two NNs that are trained via an adversarial methodology [31][32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

A neural lens for super-resolution biological imaging

et al. 2019

View full text Add to dashboard Cite

Visualizing structures smaller than the eye can see has been a driving force in scientific research since the invention of the optical microscope. Here, we use a network of neural networks to create a neural lens that has the ability to transform 20× optical microscope images into a resolution comparable to a 1500× scanning electron microscope image. In addition to magnification, the neural lens simultaneously identifies the types of objects present, and hence can label, colour-enhance and remove specific types of objects in the magnified image. The neural lens was used for the imaging of Iva xanthiifolia and Galanthus pollen grains, showing the potential for low cost, non-destructive, highresolution microscopy with automatic image processing.

show abstract

Section: Introductionmentioning

confidence: 99%

A neural lens for super-resolution biological imaging

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Recently a neural network-based oversampling approach has been used in some works in the literature to generate minority class instances. In these works, unlike SMOTE-based methods that do not take into account the data distribution, generative adversarial networks (GAN) algorithms are used for learning class-dependent data distribution and produce the generative model [16]. These techniques are mainly applied in the image processing area, where several GAN variants have been implemented in order to generate synthetic images [17].…”

Section: Related Workmentioning

confidence: 99%

Predictive Modeling of ICU Healthcare-Associated Infections from Imbalanced Data. Using Ensembles and a Clustering-Based Undersampling Approach

et al. 2019

View full text Add to dashboard Cite

Early detection of patients vulnerable to infections acquired in the hospital environment is a challenge in current health systems given the impact that such infections have on patient mortality and healthcare costs. This work is focused on both the identification of risk factors and the prediction of healthcare-associated infections in intensive-care units by means of machine-learning methods. The aim is to support decision making addressed at reducing the incidence rate of infections. In this field, it is necessary to deal with the problem of building reliable classifiers from imbalanced datasets. We propose a clustering-based undersampling strategy to be used in combination with ensemble classifiers. A comparative study with data from 4616 patients was conducted in order to validate our proposal. We applied several single and ensemble classifiers both to the original dataset and to data preprocessed by means of different resampling methods. The results were analyzed by means of classic and recent metrics specifically designed for imbalanced data classification. They revealed that the proposal is more efficient in comparison with other approaches.

show abstract

“…Of the advanced over-sampling methods, some typical or recently proposed examples are the SMOTE (Chawla et al 2002), MWMOTE (majority weighted minority over-sampling) (Barua et al 2014), graph-based over-sampling (Pé rez-Ortiz et al 2015), diversity-based over-sampling (Bennin et al 2018), and generative adversarial network-based over-sampling (Douzas and Bacao 2018) techniques. Among them, the SMOTE is the most established and widely used over-sampling method, which creates synthetic minority samples by interpolating existing minority samples along the line segments joining the k minority class nearest neighbours.…”

Section: Resampling Techniques For Class Imbalancementioning

confidence: 99%

Malicious web domain identification using online credibility and performance data by considering the class imbalance issue

Chiong

Pranata

et al. 2019

IMDS

View full text Add to dashboard Cite

Purpose -Malicious web domain identification is of significant importance to the security protection of Internet users. With online credibility and performance data, this paper aims to investigate the use of machine learning techniques for malicious web domain identification by considering the class imbalance issue (i.e., there are more benign web domains than malicious ones). Design/methodology/approach -We propose an integrated resampling approach to handle class imbalance by combining the Synthetic Minority Over-sampling TEchnique (SMOTE) and Particle Swarm Optimisation (PSO), a population-based meta-heuristic algorithm. We use the SMOTE for over-sampling and PSO for under-sampling. Findings -By applying eight well-known machine learning classifiers, the proposed integrated resampling approach is comprehensively examined using several imbalanced web domain datasets with different imbalance ratios. Compared to five other well-known resampling approaches, experimental results confirm that the proposed approach is highly effective. Practical implications -This study not only inspires the practical use of online credibility and performance data for identifying malicious web domains, but also provides an effective resampling approach for handling the class imbalance issue in the area of malicious web domain identification. Originality/value -Online credibility and performance data is applied to build malicious web domain identification models using machine learning techniques. An integrated resampling approach is proposed to address the class imbalance issue. The performance of the proposed approach is confirmed based on real-world datasets with different imbalance ratios.

show abstract

Effective data generation for imbalanced learning using conditional generative adversarial networks

Cited by 429 publications

References 24 publications

A neural lens for super-resolution biological imaging

A neural lens for super-resolution biological imaging

Predictive Modeling of ICU Healthcare-Associated Infections from Imbalanced Data. Using Ensembles and a Clustering-Based Undersampling Approach

Malicious web domain identification using online credibility and performance data by considering the class imbalance issue

Contact Info

Product

Resources

About