Deep Ensembles for Imbalanced Classification.

The proposed approach improves the model's ability to handle limited and imbalanced data. ... Several machine learning techniques for accurate detection of skin cancer from medical images have been reported. ... Conclusion For the accurate prediction of skin cancer classification an ensemble learning approach is proposed. ...

arXiv:2103.12068v4 fatcat:z5ovzovgx5h6jiprtmfmlc4lty

Multiple Versions

Imbalanced classification using deep learning has attracted much attention in intelligent fault diagnosis of machinery. ... To deal with this problem, this paper takes the advantages of ensemble learning and proposes an ensemble convolutional neural network (EnCNN) for the intelligent fault diagnosis for machines under imbalanced ... [15] proposed deep cost adaptive convolutional network for the imbalanced mechanical data classification. Wang et al. ...

doi:10.1109/access.2020.3006895 fatcat:zrhpyilntfcgjnbl6daqsydgiy

DOAJ

We evaluate our proposed ensemble deep learning framework on a large and highly imbalanced video dataset containing natural disaster events. ... Therefore, we present an ensemble deep learning framework in this paper, which not only decreases the information loss and over-fitting problems caused by single models, but also overcomes the imbalanced ... Classification As ensemble methods alleviate the over-fitting problem and increase the performance results, an enhanced ensemble deep learning algorithm is proposed in this paper. ...

doi:10.1109/ism.2016.0048 dblp:conf/ism/PouyanfarC16 fatcat:j22c3u7uyvci5bsxfpoz367sni

This work provides a comparative assessment of recent deep neural architectures for dealing with the large imbalanced data issue in the classification of pre-miRNAs. ... In particular, the computational classification of precursor microRNA (pre-miRNA) involves a high imbalance in the classes. ... In conclusion, we have provided a comparative assessment of recent deep neural approaches for dealing with a highly imbalanced data problem in bioinformatics: the classification of pre-miRNAs. ...

doi:10.1109/tnnls.2019.2914471 pmid:31170082 fatcat:ncs65hulcngfnjka4ivrlvzlqa

In this research, two deep CNN models were proposed with an ensemble technique to detect all the stages of DR by using balanced and imbalanced datasets. ... Therefore, they result in poor classification of DR stages, particularly for early stages. ... Training a deep network with imbalanced dataset may lead to biasness of classification. ...

doi:10.1155/2020/8864698 pmid:33381160 pmcid:PMC7755466 fatcat:nkbmbsv34zajxk2munqxf4vkgi

DOAJ

The occurrence of imbalanced datasets in medical imaging has proven to be a challenge for the development of models to analyze and evaluate the underlying condition. ... Multiple ResNets were extended to form an ensemble neural network model using ANNs which handles the class imbalance. ... SYSTEM DESIGN This section gives the details of the ensemble ResNet system for the binary classification of the COVID19 dataset with imbalanced classes. ...

doi:10.1109/iemtronics52119.2021.9422556 fatcat:srzpweppfrgwtajrxbstlash34

INDEX TERMS Heterogeneous sensor network, imbalanced data, ensemble deep support vector machine, support tensors machine. 27406 This work is licensed under a Creative Commons Attribution 4.0 License. ... In the other method, we extract data from different data sources and classify them with Ensemble Deep Support Vector Machine (DSVM), which combined three DSVM with different kernel functions. ... (ii) Ensemble learning and deep learning are applied to Support Vector Machine, and an Ensemble DSVM model is proposed to realize the classification of multisource heterogeneous data, which can not only ...

doi:10.1109/access.2020.2966324 fatcat:h3eta76sh5hdvphgtz76lfmwiy

DOAJ

Many malware type classification models empowered by machine and deep learning achieve superior accuracies to predict malware types.Machine learning based models need to do heavy feature engineering and ... This paper proposes an ensemble capsule network model based on bootstrap aggregating technique. ... Another crucial issue in malware classification is imbalanced datasets. Ebenuwa et al. [24] pointed imbalanced classification problem in binary classification. ...

arXiv:1912.10836v4 fatcat:mvvxu2kmfnfkpoddet3ibewm3u

Open Access Multiple Versions

Results show that our ensemble deep learning models exhibit better performance than ensembles of traditional machine learning models, especially on heavily imbalanced datasets. ... ., many studies that primarily utilize social media data, such as postings on Twitter, to study drug abuse-related activities use machine learning as a powerful tool for text classification and filtering ... Our ensemble deep learning model combines wordlevel CNN models and character-level CNN models to perform classification. ...

doi:10.3233/shti190204 pmid:31437906 fatcat:yxx7ne7xb5ckdhwqm4zx75avxi

Results show that our ensemble deep learning models exhibit better performance than ensembles of traditional machine learning models, especially on heavily imbalanced datasets. ... ., many studies that primarily utilize social media data, such as postings on Twitter, to study drug abuse-related activities use machine learning as a powerful tool for text classification and filtering ... Our ensemble deep learning model combines wordlevel CNN models and character-level CNN models to perform classification. ...

arXiv:1904.02062v1 fatcat:vdmfs4rctnde5dbixgbna2i3xy

Therefore, we aim to address the problem of class imbalance in deep learning for smart home data. We assess it with Activities of Daily Living recognition using binary sensors dataset. ... Deep learning algorithms perform well on balanced datasets, yet their performance cannot be promised on imbalanced datasets. ... Since the classes of the datasets are imbalanced, we propose synthetic minority oversampling technique (SMOTE) as input data for the deep learning model. ...

doi:10.1007/s42979-020-00211-1 fatcat:kqhvrp5pmjbhtogf2v7s5wnfia

Open Access

Since there are far more healthy people than infected patients, this classification problem faces the challenge of imbalanced data. ... In this paper, we propose a deep residual network-based method, namely the DiCOVA-Net, to identify COVID-19 infected patients based on the acoustic recording of their coughs. ... Acknowledgements We are appreciative to the DiCOVA 2021 Challenge organizers for their efforts in providing participants with data and a platform for the competition. ...

arXiv:2107.06126v2 fatcat:ax4gkcrhfveh7nxug6pqhujq2q

Open Access Multiple Versions

In this paper, we propose a heterogeneous ensemble learning model based on data distribution (HELMDD) to deal with imbalanced data in CCFD. ... Class imbalance in credit card transaction data is a primary factor affecting the classification performance of current detection models. ... Consequently, they proposed an ensemble learning model based on RF and FNN. Deep learning for CCFD has been discussed in several works [20, 31, 32] . Rushin et al. ...

doi:10.1155/2021/2531210 fatcat:cjwjrdq43fhcbhnnxhf5zclngi

DOAJ

The new representations are fed into an ensemble deep learning attack detection model specifically designed for an ICS environment. ... In this paper, we propose a deep representation learning model to construct new balanced representations of the imbalanced dataset. ... The proposed model generates a new balanced representation from a raw dataset and feeds it to an ensemble deep learning model for classification. ...

arXiv:2005.00936v1 fatcat:n3tqa2hpxfbcjnfi7g3xrvqyzi

In this paper, we explore many kinds of deep convolution neural network architectures for large-scale product recognition task, which is heavily class-imbalanced and noisy labeled data, making it more ... Together with ensemble technology and negative learning loss for noisy labeled data, we further improve the model performance on online test data. ... classification. ...

arXiv:2007.01618v1 fatcat:nvdwxsomprh2ra262ecvzf6aou

Transfer Learning with Ensembles of Deep Neural Networks for Skin Cancer Detection in Imbalanced Data Sets [article]

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Deep Neural Network Ensemble for the Intelligent Fault Diagnosis of Machines under Imbalanced Data

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Semantic Event Detection Using Ensemble Deep Learning

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Deep Neural Architectures for Highly Imbalanced Data in Bioinformatics

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Ensemble Framework of Deep CNNs for Diabetic Retinopathy Detection

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COVID-19 Identification in CLAHE Enhanced CT Scans with Class Imbalance using Ensembled ResNets

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Imbalanced Data Classification for Multi-source Heterogenous Sensor Networks

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Random CapsNet Forest Model for Imbalanced Malware Type Classification Task [article]

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An Ensemble Deep Learning Model for Drug Abuse Detection in Sparse Twitter-Sphere

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An Ensemble Deep Learning Model for Drug Abuse Detection in Sparse Twitter-Sphere [article]

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Efficacy of Imbalanced Data Handling Methods on Deep Learning for Smart Homes Environments

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DiCOVA-Net: Diagnosing COVID-19 using Acoustics based on Deep Residual Network for the DiCOVA Challenge 2021 [article]

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A Heterogeneous Ensemble Learning Model Based on Data Distribution for Credit Card Fraud Detection

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An Ensemble Deep Learning-based Cyber-Attack Detection in Industrial Control System [article]

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Balanced Symmetric Cross Entropy for Large Scale Imbalanced and Noisy Data [article]

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