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Efficient training on biased minimax probability machine for imbalanced text classification

Xiang Peng, Irwin King
<span title="">2007</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/s4hirppq3jalbopssw22crbwwa" style="color: black;">Proceedings of the 16th international conference on World Wide Web - WWW &#39;07</a> </i> &nbsp;
The Biased Minimax Probability Machine (BMPM) constructs a classifier which deals with the imbalanced learning tasks.  ...  We outline the theoretical derivatives of the biased classification model, and address the text classification tasks where negative training documents significantly outnumber the positive ones using the  ...  In this paper, we apply the model of Biased Minimax Probability Machine (BMPM) to the problem of imbalanced text classification, and propose a new training algorithm to tackle the complexity and accuracy  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/1242572.1242741">doi:10.1145/1242572.1242741</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/www/PengK07.html">dblp:conf/www/PengK07</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/oy3ytghocbe45hvxyoeeqwlxiu">fatcat:oy3ytghocbe45hvxyoeeqwlxiu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20080221154401/http://www.cse.cuhk.edu.hk/~king/PUB/WWW2007_Peng.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/ff/aa/ffaaf677d2291dd692564a27d1f2e7f20c879de6.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/1242572.1242741"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> acm.org </button> </a>

Large Scale Imbalanced Classification with Biased Minimax Probability Machine

Xiang Peng, Irwin King
<span title="">2007</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/qwvlxlyjhjb5fncg2asafcm5hm" style="color: black;">Neural Networks (IJCNN), International Joint Conference on</a> </i> &nbsp;
The Biased Minimax Probability Machine (BMPM) constructs a classifier which deals with the imbalanced learning tasks.  ...  In this paper, we apply the biased classification model to large scale imbalanced classification problem, and develop a critical extension to train the BMPM efficiently which is a novel training algorithm  ...  CUHK4235/04E) and is affiliated with the Microsoft-CUHK Joint Laboratory for Human-centric Computing and Interface Technologies.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/ijcnn.2007.4371211">doi:10.1109/ijcnn.2007.4371211</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/ijcnn/PengK07.html">dblp:conf/ijcnn/PengK07</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/aifl3eib2nhuzetpopb4hxwegq">fatcat:aifl3eib2nhuzetpopb4hxwegq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20080221154434/http://www.cse.cuhk.edu.hk/~king/PUB/IJCNN2007_Peng1433.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/41/74/4174af3f1a1f6865dfc4df746f016a8b5a7b0e9c.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/ijcnn.2007.4371211"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Robust BMPM training based on second-order cone programming and its application in medical diagnosis

Xiang Peng, Irwin King
<span title="">2008</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/oml24fsyizfuhn3rn5np75ubdi" style="color: black;">Neural Networks</a> </i> &nbsp;
The Biased Minimax Probability Machine (BMPM) constructs a classifier which deals with the imbalanced learning tasks.  ...  In this paper, we develop a novel yet critical extension training algorithm for BMPM that is based on Second-Order Cone Programming (SOCP).  ...  Lanckriet for providing the Matlab source code of the MPM on the web, and Kaizhu  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.neunet.2007.12.051">doi:10.1016/j.neunet.2007.12.051</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/18282689">pmid:18282689</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/daq3hcwnvfdo7imv3bbn5e5lga">fatcat:daq3hcwnvfdo7imv3bbn5e5lga</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20110401063948/http://www.cse.cuhk.edu.hk/~king/PUB/nn2008.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/07/f7/07f79ee76140c319cb8136a510a7e551f7984d55.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.neunet.2007.12.051"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> elsevier.com </button> </a>

Biased Minimax Probability Machine Active Learning for Relevance Feedback in Content-Based Image Retrieval [chapter]

Xiang Peng, Irwin King
<span title="">2006</span> <i title="Springer Berlin Heidelberg"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
In this paper we apply Biased Minimax Probability Machine (BMPM) to address the problem of relevance feedback in Content-based Image Retrieval (CBIR).  ...  Experiments are performed to evaluate the efficiency of our method, and promising experimental results are obtained.  ...  CUHK4235/04E) and is affiliated with the Microsoft-CUHK Joint Laboratory for Human-centric Computing and Interface Technologies.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/11875581_114">doi:10.1007/11875581_114</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wzrygg76qrg4xhrz5yugumnhre">fatcat:wzrygg76qrg4xhrz5yugumnhre</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20080419002126/http://www.cse.cuhk.edu.hk/~king/PUB/IDEAL2006_Peng.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/ea/31/ea31d2599a7fcf56c7f96faec48becf98b547513.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/11875581_114"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

Class-Weighted Classification: Trade-offs and Robust Approaches [article]

Ziyu Xu, Chen Dan, Justin Khim, Pradeep Ravikumar
<span title="2020-05-26">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We address imbalanced classification, the problem in which a label may have low marginal probability relative to other labels, by weighting losses according to the correct class.  ...  Finally, we empirically demonstrate the efficacy of LCVaR and LHCVaR on improving class conditional risks.  ...  A Organization Our appendices contain proofs, all of which are omitted from the main text, and additional details on the weighting approach to imbalanced classification.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2005.12914v1">arXiv:2005.12914v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/2keuzsj5djei7lyss2kwe4cl3i">fatcat:2keuzsj5djei7lyss2kwe4cl3i</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200529073930/https://arxiv.org/pdf/2005.12914v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2005.12914v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Towards Sustainable Energy Efficiency with Intelligent Electricity Theft Detection in Smart Grids Emphasising Enhanced Neural Networks

Abdulaziz Aldegheishem, Mubbashara Anwar, Nadeem Javaid, Nabil Alrajeh, Muhammad Shafiq, Hasan Ahmed
<span title="">2021</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
Finally, a light gradient boosting model is used for classification purpose.  ...  Finally, adaptive boosting is used for classification of honest and suspicious consumers.  ...  GAN is based on minimax, which means gradient descent is used for training both discriminator and generator networks.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2021.3056566">doi:10.1109/access.2021.3056566</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3yfl4bleivaztmqb4ooqi5cjyq">fatcat:3yfl4bleivaztmqb4ooqi5cjyq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210715013603/https://ieeexplore.ieee.org/ielx7/6287639/9312710/09344652.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/fd/aa/fdaa56d0e3d2c49beeed1ea272e3f14d39913443.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2021.3056566"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> ieee.com </button> </a>

Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning [chapter]

Hui Han, Wen-Yuan Wang, Bing-Huan Mao
<span title="">2005</span> <i title="Springer Berlin Heidelberg"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
For the minority class, experiments show that our approaches achieve better TP rate and F-value than SMOTE and random over-sampling methods.  ...  Synthetic minority oversampling technique (SMOTE) is one of the over-sampling methods addressing this problem.  ...  Kaizhu Huang et al. presented Biased Minimax Probability Machine (BMPM) to resolve the imbalance problem.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/11538059_91">doi:10.1007/11538059_91</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/7bhqslgcurggxcgv7qdkhsxfkq">fatcat:7bhqslgcurggxcgv7qdkhsxfkq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170809144447/http://sci2s.ugr.es/keel/keel-dataset/pdfs/2005-Han-LNCS.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/e0/20/e020a6e052468bf4537914c87918a8e25080fc31.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/11538059_91"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

Assessing Dataset Bias in Computer Vision [article]

Athiya Deviyani
<span title="2022-05-03">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We then trained a classifier for each of the augmented datasets and evaluated their performance on the native test set and on external facial recognition datasets.  ...  This signifies that the model was also able to mitigate the biases present in the baseline model that was trained on the original training set.  ...  semi-supervised text classification [76] , text generation [63] and fake news detection [37] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2205.01811v1">arXiv:2205.01811v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/nerm3uxlbngqfbi7fsll6zjtre">fatcat:nerm3uxlbngqfbi7fsll6zjtre</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220506112846/https://arxiv.org/pdf/2205.01811v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/2a/81/2a8179c3f9e4679ad40ef015d2ac6cbab7af6e05.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2205.01811v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics

Victoria López, Alberto Fernández, Salvador García, Vasile Palade, Francisco Herrera
<span title="">2013</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ozlq63ehnjeqxf6cuxxn27cqra" style="color: black;">Information Sciences</a> </i> &nbsp;
Palade, Efficient resampling methods for training support vector machines with imbalanced datasets, in: Proceedings of the 2010 International Joint Conference on Neural Networks (IJCNN), 2010.  ...  Tourassi, Training neural network classifiers for medical decision making: the effects of imbalanced datasets on classification performance, Neural Networks 21 (2–3) (2008). [92] G.J.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.ins.2013.07.007">doi:10.1016/j.ins.2013.07.007</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/dtuhxqu7hzfclktmqlci5ykiw4">fatcat:dtuhxqu7hzfclktmqlci5ykiw4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170808002026/http://digibug.ugr.es/bitstream/10481/34132/1/23799304.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/38/40/384038f3c7ac80c5d40307ef116ab11a65671b82.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.ins.2013.07.007"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> elsevier.com </button> </a>

Alleviating Class Imbalance in Actuarial Applications Using Generative Adversarial Networks

Kwanda Sydwell Ngwenduna, Rendani Mbuvha
<span title="2021-03-08">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/jgmncbgvj5ffna5sozuvnv2jr4" style="color: black;">Risks</a> </i> &nbsp;
Overall, we show a significant superiority of GANs for boosting predictive models when compared to competing approaches on benchmark data sets.  ...  Another common pathology when applying machine learning techniques in actuarial domains is the prevalence of imbalanced classes where risk events of interest, such as mortality and fraud, are under-represented  ...  Acknowledgments: The authors would like to thank the reviewers for their helpful comments.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/risks9030049">doi:10.3390/risks9030049</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/4oxdwjws5bd5vpezjamuyjc6xy">fatcat:4oxdwjws5bd5vpezjamuyjc6xy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210309224617/https://res.mdpi.com/d_attachment/risks/risks-09-00049/article_deploy/risks-09-00049-v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/16/8c/168cbf891fb94765c49adfd0d857fbbc36f44655.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/risks9030049"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a>

Learning Robust Models for e-Commerce Product Search [article]

Thanh V. Nguyen, Nikhil Rao, Karthik Subbian
<span title="2020-05-07">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
On live search traffic, our model gains significant improvement in multiple countries.  ...  These mismatches result from counterfactual biases of the ranking algorithms toward noisy behavioral signals such as clicks and purchases in the search logs.  ...  Hotflip: White-box adversarial examples for text classification. arXiv preprint arXiv:1712.06751.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2005.03624v1">arXiv:2005.03624v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/7pobpyubcrbfjlz4k4amgw4c2m">fatcat:7pobpyubcrbfjlz4k4amgw4c2m</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200509012209/https://arxiv.org/pdf/2005.03624v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2005.03624v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review

Dhiraj Neupane, Jongwon Seok
<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
This paper, we believe, can be of good help for future researchers to start their work on machinery fault detection and diagnosis using the CWRU dataset.  ...  INDEX TERMS Bearing, deep learning, machine learning, machinery fault detection and diagnosis, CWRU dataset. 93156 VOLUME 8, 2020  ...  ACKNOWLEDGMENT The authors thank Case Western Reserve University for providing free access to the bearing vibration experimental data from their website.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2020.2990528">doi:10.1109/access.2020.2990528</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/252hcj5d5bftxedititg2ya7sm">fatcat:252hcj5d5bftxedititg2ya7sm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201108115316/https://ieeexplore.ieee.org/ielx7/6287639/8948470/09078761.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/2b/a9/2ba9ec71a675e5d678215e06af9b2fe8e13c7c40.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2020.2990528"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> ieee.com </button> </a>

Over-sampling algorithm for imbalanced data classification

Xiaolong XU, Wen CHEN, Yanfei SUN
<span title="">2019</span> <i title="Journal of Systems Engineering and Electronics"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/dm5p322bw5f5hfwucl2tiiuljq" style="color: black;">Journal of Systems Engineering and Electronics</a> </i> &nbsp;
For imbalanced datasets, the focus of classification is to identify samples of the minority class.  ...  The performance of current data mining algorithms is not good enough for processing imbalanced datasets.  ...  [15] proposed a biased minimax probability machine (BMPM) algorithm.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.21629/jsee.2019.06.12">doi:10.21629/jsee.2019.06.12</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/alzzj73qangljlixlkoz3hx6cq">fatcat:alzzj73qangljlixlkoz3hx6cq</a> </span>
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Network Intrusion Detection Based on Supervised Adversarial Variational Auto-Encoder with Regularization

Yanqing Yang, Kangfeng Zheng, Bin Wu, Yixian Yang, Xiujuan Wang
<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
SAVAER's decoder is used to synthesize samples of low-frequent and unknown attacks, thereby increasing the diversity of training samples and balancing the training data set.  ...  The experimental results show that the proposed SAVAER-DNN is more suitable for data augmentation than the other three well-known data oversampling methods.  ...  Imbalanced data will make the classifier be biased toward the majority class, resulting in a high false positive rate for the minority attacks.  ... 
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<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201103084257/https://ieeexplore.ieee.org/ielx7/6287639/8948470/09017945.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/a2/5e/a25eeb628f324f3677cc095227c3ca16a7d9ccf4.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2020.2977007"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> ieee.com </button> </a>

Ballpark Crowdsourcing: The Wisdom of Rough Group Comparisons [article]

Tom Hope, Dafna Shahaf
<span title="2017-12-13">2017</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
By collecting rough guesses on groups of instances and using machine learning to infer the individual labels, our lightweight framework is able to address core crowdsourcing challenges and train machine  ...  Crowdsourcing has become a popular method for collecting labeled training data.  ...  BMP (Biased Minimax Probability Machine) is a method proposed in [11] for handling imbalanced classification tasks, reported for the 1978 cohort only.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1712.04828v1">arXiv:1712.04828v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/abogfreg6vewjac5vphulwrdeu">fatcat:abogfreg6vewjac5vphulwrdeu</a> </span>
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