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

Aqsa Saeed Qureshi, Teemu Roos
2021 arXiv   pre-print
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

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

Feng Jia, Shihao Li, Hao Zuo, Jianjun Shen
2020 IEEE Access  
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

Semantic Event Detection Using Ensemble Deep Learning

Samira Pouyanfar, Shu-Ching Chen
2016 2016 IEEE International Symposium on Multimedia (ISM)  
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

Deep Neural Architectures for Highly Imbalanced Data in Bioinformatics

Leandro A. Bugnon, Cristian Yones, Diego H. Milone, Georgina Stegmayer
2019 IEEE Transactions on Neural Networks and Learning Systems  
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

Ensemble Framework of Deep CNNs for Diabetic Retinopathy Detection

Gao Jinfeng, Sehrish Qummar, Zhang Junming, Yao Ruxian, Fiaz Gul Khan, Elpida Keravnou
2020 Computational Intelligence and Neuroscience  
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

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

Sowmya Sanagavarapu, Sashank Sridhar, T.V. Gopal
2021 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS)  
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

Imbalanced Data Classification for Multi-source Heterogenous Sensor Networks

Wei Wang, Mengjun Zhang, Li Zhang, Qiong Bai
2020 IEEE Access  
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

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

Aykut Çayır, Uğur Ünal, Hasan Dağ
2020 arXiv   pre-print
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

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

Han Hu, NhatHai Phan, James Geller, Stephen Iezzi, Huy Vo, Dejing Dou, Soon Ae Chun
2019 Studies in Health Technology and Informatics  
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

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

Han Hu and NhatHai Phan and James Geller and Stephen Iezzi and Huy Vo and Dejing Dou and Soon Ae Chun
2019 arXiv   pre-print
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

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

Rebeen Ali Hamad, Masashi Kimura, Jens Lundström
2020 SN Computer Science  
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

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

Jiangeng Chang, Shaoze Cui, Mengling Feng
2022 arXiv   pre-print
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

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

Yalong Xie, Aiping Li, Liqun Gao, Ziniu Liu, Shan Zhong
2021 Wireless Communications and Mobile Computing  
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

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

Abdulrahman Al-Abassi and Hadis Karimipour
2020 arXiv   pre-print
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

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

Feifei Huang, Jie Li, Xuelin Zhu
2020 arXiv   pre-print
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
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