{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T06:36:25Z","timestamp":1780554985372,"version":"3.54.1"},"reference-count":105,"publisher":"Association for Computing Machinery (ACM)","issue":"3","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2022,11]]},"abstract":"<jats:p>\n            Epilepsy is a chronic neurological disease, ranked as the second most burdensome neurological disorder worldwide. Detecting Interictal Epileptiform Discharges (IEDs) is among the most important clinician operations to support epilepsy diagnosis, rendering automatic IED detection based on electroencephalography (EEG) signals an important topic. However, most existing solutions were designed and evaluated upon artificially balanced IED datasets, which do not conform to the real-world highly imbalanced scenarios. In this work, we propose the iEDeaL framework for automatic IED detection in challenging real-world use cases. The main components of iEDeaL are the new SC neural network architecture, to efficiently detect IEDs on raw EEG series instead of extracted features, and SaSu, a novel loss function to train SC by optimizing the\n            <jats:italic>F<\/jats:italic>\n            <jats:sub>\u03b2<\/jats:sub>\n            -score. Experiments on two real-world imbalanced IED datasets verify the advantages of iEDeaL in offering more accurate and efficient IED detection when compared with other state-of-the-art deep learning-based and spectrogram feature-based solutions.\n          <\/jats:p>","DOI":"10.14778\/3570690.3570698","type":"journal-article","created":{"date-parts":[[2023,1,23]],"date-time":"2023-01-23T17:29:55Z","timestamp":1674494995000},"page":"480-490","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":14,"title":["iEDeaL"],"prefix":"10.14778","volume":"16","author":[{"given":"Qitong","family":"Wang","sequence":"first","affiliation":[{"name":"Universit\u00e9 Paris Cit\u00e9"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stephen","family":"Whitmarsh","sequence":"additional","affiliation":[{"name":"Sorbonne Universit\u00e9"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vincent","family":"Navarro","sequence":"additional","affiliation":[{"name":"Sorbonne Universit\u00e9"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Themis","family":"Palpanas","sequence":"additional","affiliation":[{"name":"Universit\u00e9 Paris Cit\u00e9 &amp; French University Institute (IUF)"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,1,23]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNSRE.2017.2755770"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinph.2017.06.252"},{"key":"e_1_2_1_3_1","volume-title":"Katsaggelos","author":"Bahaadini Sara","year":"2018","unstructured":"Sara Bahaadini , Vahid Noroozi , Neda Rohani , Scott Coughlin , Michael Zevin , Joshua R. Smith , Vicky Kalogera , and Aggelos K . Katsaggelos . 2018 . Machine learning for Gravity Spy: Glitch classification and dataset. Information Sciences ( 2018). Sara Bahaadini, Vahid Noroozi, Neda Rohani, Scott Coughlin, Michael Zevin, Joshua R. Smith, Vicky Kalogera, and Aggelos K. Katsaggelos. 2018. Machine learning for Gravity Spy: Glitch classification and dataset. Information Sciences (2018)."},{"key":"e_1_2_1_4_1","volume-title":"An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling. arXiv","author":"Bai Shaojie","year":"2018","unstructured":"Shaojie Bai , J. Zico Kolter , and Vladlen Koltun . 2018. An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling. arXiv ( 2018 ). Shaojie Bai, J. Zico Kolter, and Vladlen Koltun. 2018. An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling. arXiv (2018)."},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinph.2011.09.023"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-017-02577-y"},{"key":"e_1_2_1_7_1","doi-asserted-by":"crossref","unstructured":"Yoshua Bengio J\u00e9r\u00f4me Louradour Ronan Collobert and Jason Weston. 2009. Curriculum learning. In ICML.  Yoshua Bengio J\u00e9r\u00f4me Louradour Ronan Collobert and Jason Weston. 2009. Curriculum learning. In ICML.","DOI":"10.1145\/1553374.1553380"},{"key":"e_1_2_1_8_1","doi-asserted-by":"crossref","unstructured":"Paul Boniol Mohammed Meftah Emmanuel Remy and Themis Palpanas. 2022. dCAM: Dimension-wise Class Activation Map for Explaining Multivariate Data Series Classification. In SIGMOD.  Paul Boniol Mohammed Meftah Emmanuel Remy and Themis Palpanas. 2022. dCAM: Dimension-wise Class Activation Map for Explaining Multivariate Data Series Classification. In SIGMOD.","DOI":"10.1145\/3514221.3526183"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinph.2007.04.017"},{"key":"e_1_2_1_10_1","volume-title":"David Yew-Kwong Woon, and See-Kiong Ng","author":"Cao Hong","year":"2011","unstructured":"Hong Cao , Xiaoli Li , David Yew-Kwong Woon, and See-Kiong Ng . 2011 . SPO : Structure Preserving Oversampling for Imbalanced Time Series Classification. In ICDM. Hong Cao, Xiaoli Li, David Yew-Kwong Woon, and See-Kiong Ng. 2011. SPO: Structure Preserving Oversampling for Imbalanced Time Series Classification. In ICDM."},{"key":"e_1_2_1_11_1","volume-title":"David Yew-Kwong Woon, and See-Kiong Ng","author":"Cao Hong","year":"2013","unstructured":"Hong Cao , Xiaoli Li , David Yew-Kwong Woon, and See-Kiong Ng . 2013 . Integrated Oversampling for Imbalanced Time Series Classification. TKDE ( 2013). Hong Cao, Xiaoli Li, David Yew-Kwong Woon, and See-Kiong Ng. 2013. Integrated Oversampling for Imbalanced Time Series Classification. TKDE (2013)."},{"key":"e_1_2_1_12_1","volume-title":"Smile: A System to Support Machine Learning on EEG Data at Scale. PVLDB","author":"Cao Lei","year":"2019","unstructured":"Lei Cao , Wenbo Tao , Sungtae An , Jing Jin , Yizhou Yan , Xiaoyu Liu , Wendong Ge , Adam Sah , Leilani Battle , Jimeng Sun , Remco Chang , M. Brandon Westover , Samuel Madden , and Michael Stonebraker . 2019 . Smile: A System to Support Machine Learning on EEG Data at Scale. PVLDB (2019). Lei Cao, Wenbo Tao, Sungtae An, Jing Jin, Yizhou Yan, Xiaoyu Liu, Wendong Ge, Adam Sah, Leilani Battle, Jimeng Sun, Remco Chang, M. Brandon Westover, Samuel Madden, and Michael Stonebraker. 2019. Smile: A System to Support Machine Learning on EEG Data at Scale. PVLDB (2019)."},{"key":"e_1_2_1_13_1","volume-title":"Lower bounds for finding stationary points II: first-order methods. Mathematical Programming","author":"Carmon Yair","year":"2021","unstructured":"Yair Carmon , John C. Duchi , Oliver Hinder , and Aaron Sidford . 2021. Lower bounds for finding stationary points II: first-order methods. Mathematical Programming ( 2021 ). Yair Carmon, John C. Duchi, Oliver Hinder, and Aaron Sidford. 2021. Lower bounds for finding stationary points II: first-order methods. Mathematical Programming (2021)."},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(12)61689-4"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1093\/brain\/awz386"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.3389\/fneur.2021.603868"},{"key":"e_1_2_1_17_1","volume-title":"Belongie","author":"Cui Yin","year":"2019","unstructured":"Yin Cui , Menglin Jia , Tsung-Yi Lin , Yang Song , and Serge J . Belongie . 2019 . Class-Balanced Loss Based on Effective Number of Samples. In CVPR. Yin Cui, Menglin Jia, Tsung-Yi Lin, Yang Song, and Serge J. Belongie. 2019. Class-Balanced Loss Based on Effective Number of Samples. In CVPR."},{"key":"e_1_2_1_18_1","volume-title":"Machine learning for detection of interictal epileptiform discharges. Clinical Neurophysiology","author":"da Silva Louren\u00e7o Catarina","year":"2021","unstructured":"Catarina da Silva Louren\u00e7o , Marleen C Tjepkema-Cloostermans , and Michel JAM van Putten . 2021. Machine learning for detection of interictal epileptiform discharges. Clinical Neurophysiology ( 2021 ). Catarina da Silva Louren\u00e7o, Marleen C Tjepkema-Cloostermans, and Michel JAM van Putten. 2021. Machine learning for detection of interictal epileptiform discharges. Clinical Neurophysiology (2021)."},{"key":"e_1_2_1_19_1","unstructured":"Krzysztof Dembczynski Willem Waegeman Weiwei Cheng and Eyke H\u00fcllermeier. 2011. An Exact Algorithm for F-Measure Maximization. In NIPS.  Krzysztof Dembczynski Willem Waegeman Weiwei Cheng and Eyke H\u00fcllermeier. 2011. An Exact Algorithm for F-Measure Maximization. In NIPS."},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.14778\/3547305.3547308"},{"key":"e_1_2_1_21_1","unstructured":"Charles Elkan. 2001. The foundations of cost-sensitive learning. In IJCAI.  Charles Elkan. 2001. The foundations of cost-sensitive learning. In IJCAI."},{"key":"e_1_2_1_22_1","volume-title":"Deep learning for time series classification: a review. DMKD","author":"Fawaz Hassan Ismail","year":"2019","unstructured":"Hassan Ismail Fawaz , Germain Forestier , Jonathan Weber , Lhassane Idoumghar , and Pierre-Alain Muller . 2019. Deep learning for time series classification: a review. DMKD ( 2019 ). Hassan Ismail Fawaz, Germain Forestier, Jonathan Weber, Lhassane Idoumghar, and Pierre-Alain Muller. 2019. Deep learning for time series classification: a review. DMKD (2019)."},{"key":"e_1_2_1_23_1","volume-title":"InceptionTime: Finding AlexNet for time series classification. DMKD","author":"Fawaz Hassan Ismail","year":"2020","unstructured":"Hassan Ismail Fawaz , Benjamin Lucas , Germain Forestier , Charlotte Pelletier , Daniel F. Schmidt , Jonathan Weber , Geoffrey I. Webb , Lhassane Idoumghar , Pierre-Alain Muller , and Fran\u00e7ois Petitjean . 2020. InceptionTime: Finding AlexNet for time series classification. DMKD ( 2020 ). Hassan Ismail Fawaz, Benjamin Lucas, Germain Forestier, Charlotte Pelletier, Daniel F. Schmidt, Jonathan Weber, Geoffrey I. Webb, Lhassane Idoumghar, Pierre-Alain Muller, and Fran\u00e7ois Petitjean. 2020. InceptionTime: Finding AlexNet for time series classification. DMKD (2020)."},{"key":"e_1_2_1_24_1","volume-title":"Ensemble of Classifiers Based on Multiobjective Genetic Sampling for Imbalanced Data. TKDE","author":"Fernandes Everlandio R. Q.","year":"2020","unstructured":"Everlandio R. Q. Fernandes , Andr\u00e9 C. P. L. F. de Carvalho , and Xin Yao . 2020. Ensemble of Classifiers Based on Multiobjective Genetic Sampling for Imbalanced Data. TKDE ( 2020 ). Everlandio R. Q. Fernandes, Andr\u00e9 C. P. L. F. de Carvalho, and Xin Yao. 2020. Ensemble of Classifiers Based on Multiobjective Genetic Sampling for Imbalanced Data. TKDE (2020)."},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2019.8682196"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinph.2020.02.032"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/abf28e"},{"key":"e_1_2_1_28_1","volume-title":"Silvia Lopez de Diego, Iyad Obeid, and Joseph Picone.","author":"Golmohammadi Meysam","year":"2019","unstructured":"Meysam Golmohammadi , Amir Hossein Harati Nejad Torbati , Silvia Lopez de Diego, Iyad Obeid, and Joseph Picone. 2019 . Automatic Analysis of EEGs Using Big Data and Hybrid Deep Learning Architectures. Frontiers in Human Neuroscience ( 2019). Meysam Golmohammadi, Amir Hossein Harati Nejad Torbati, Silvia Lopez de Diego, Iyad Obeid, and Joseph Picone. 2019. Automatic Analysis of EEGs Using Big Data and Hybrid Deep Learning Architectures. Frontiers in Human Neuroscience (2019)."},{"key":"e_1_2_1_29_1","volume-title":"Deep learning","author":"Goodfellow Ian","unstructured":"Ian Goodfellow , Yoshua Bengio , and Aaron Courville . 2016. Deep learning . MIT press . Ian Goodfellow, Yoshua Bengio, and Aaron Courville. 2016. Deep learning. MIT press."},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1097\/WNP.0000000000000344"},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jneumeth.2012.11.005"},{"key":"e_1_2_1_32_1","doi-asserted-by":"crossref","unstructured":"Amir Harati Meysam Golmohammadi Silvia Lopez Iyad Obeid and Joseph Picone. 2015. Improved EEG event classification using differential energy. In SPMB.  Amir Harati Meysam Golmohammadi Silvia Lopez Iyad Obeid and Joseph Picone. 2015. Improved EEG event classification using differential energy. In SPMB.","DOI":"10.1109\/SPMB.2015.7405421"},{"key":"e_1_2_1_33_1","unstructured":"Kaiming He Xiangyu Zhang Shaoqing Ren and Jian Sun. 2016. Deep Residual Learning for Image Recognition. In CVPR.  Kaiming He Xiangyu Zhang Shaoqing Ren and Jian Sun. 2016. Deep Residual Learning for Image Recognition. In CVPR."},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1097\/WNP.0000000000000806"},{"key":"e_1_2_1_35_1","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1097\/WNP.0b013e3182784729","article-title":"American Clinical Neurophysiology Society's Standardized Critical Care EEG Terminology: 2012 Version","volume":"30","author":"Hirsch L J","year":"2013","unstructured":"L J Hirsch , S M LaRoche , N Gaspard , E Gerard , A Svoronos , S T Herman , R Mani , H Arif , N Jette , Y Minazad , J F Kerrigan , P Vespa , S Hantus , J Claassen , G B Young , E So , P W Kaplan , M R Nuwer , N B Fountain , and F W Drislane . 2013 . American Clinical Neurophysiology Society's Standardized Critical Care EEG Terminology: 2012 Version . Journal of Clinical Neurophysiology 30 , 1 (2013), 27 . L J Hirsch, S M LaRoche, N Gaspard, E Gerard, A Svoronos, S T Herman, R Mani, H Arif, N Jette, Y Minazad, J F Kerrigan, P Vespa, S Hantus, J Claassen, G B Young, E So, P W Kaplan, M R Nuwer, N B Fountain, and F W Drislane. 2013. American Clinical Neurophysiology Society's Standardized Critical Care EEG Terminology: 2012 Version. Journal of Clinical Neurophysiology 30, 1 (2013), 27.","journal-title":"Journal of Clinical Neurophysiology"},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1016\/0013-4694(92)90126-3"},{"key":"e_1_2_1_37_1","volume-title":"Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. In ICML.","author":"Ioffe Sergey","year":"2015","unstructured":"Sergey Ioffe and Christian Szegedy . 2015 . Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. In ICML. Sergey Ioffe and Christian Szegedy. 2015. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. In ICML."},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10548-014-0379-1"},{"key":"e_1_2_1_39_1","doi-asserted-by":"crossref","unstructured":"Martin Jansche. 2005. Maximum expected F-measure training of logistic regression models. In HLT-EMNLP.  Martin Jansche. 2005. Maximum expected F-measure training of logistic regression models. In HLT-EMNLP.","DOI":"10.3115\/1220575.1220662"},{"key":"e_1_2_1_40_1","volume-title":"Random coverings in several dimensions. Acta Mathematica","author":"Janson Svante","year":"1986","unstructured":"Svante Janson . 1986. Random coverings in several dimensions. Acta Mathematica ( 1986 ). Svante Janson. 1986. Random coverings in several dimensions. Acta Mathematica (1986)."},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1001\/jamaneurol.2019.3531"},{"key":"e_1_2_1_42_1","unstructured":"Purushottam Kar Harikrishna Narasimhan and Prateek Jain. 2014. Online and Stochastic Gradient Methods for Non-decomposable Loss Functions. In NIPS.  Purushottam Kar Harikrishna Narasimhan and Prateek Jain. 2014. Online and Stochastic Gradient Methods for Non-decomposable Loss Functions. In NIPS."},{"key":"e_1_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1093\/brain\/aww019"},{"key":"e_1_2_1_44_1","volume-title":"Dhillon","author":"Koyejo Oluwasanmi","year":"2014","unstructured":"Oluwasanmi Koyejo , Nagarajan Natarajan , Pradeep Ravikumar , and Inderjit S . Dhillon . 2014 . Consistent Binary Classification with Generalized Performance Metrics. In NIPS. Oluwasanmi Koyejo, Nagarajan Natarajan, Pradeep Ravikumar, and Inderjit S. Dhillon. 2014. Consistent Binary Classification with Generalized Performance Metrics. In NIPS."},{"key":"e_1_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10439-014-1004-x"},{"key":"e_1_2_1_46_1","volume-title":"A surrogate loss function for optimization of F\u03b2 score in binary classification with imbalanced data. arXiv 2104.01459","author":"Lee Namgil","year":"2021","unstructured":"Namgil Lee , Heejung Yang , and Hojin Yoo . 2021. A surrogate loss function for optimization of F\u03b2 score in binary classification with imbalanced data. arXiv 2104.01459 ( 2021 ). Namgil Lee, Heejung Yang, and Hojin Yoo. 2021. A surrogate loss function for optimization of F\u03b2 score in binary classification with imbalanced data. arXiv 2104.01459 (2021)."},{"key":"e_1_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2022.119116"},{"key":"e_1_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2018.08.020"},{"key":"e_1_2_1_49_1","volume-title":"Entropy-based Sampling Approaches for Multi-Class Imbalanced Problems. TKDE","author":"Li Lusi","year":"2020","unstructured":"Lusi Li , Haibo He , and Jie Li. 2020. Entropy-based Sampling Approaches for Multi-Class Imbalanced Problems. TKDE ( 2020 ). Lusi Li, Haibo He, and Jie Li. 2020. Entropy-based Sampling Approaches for Multi-Class Imbalanced Problems. TKDE (2020)."},{"key":"e_1_2_1_50_1","unstructured":"Tsung-Yi Lin Priya Goyal Ross B. Girshick Kaiming He and Piotr Doll\u00e1r. 2017. Focal Loss for Dense Object Detection. In ICCV.  Tsung-Yi Lin Priya Goyal Ross B. Girshick Kaiming He and Piotr Doll\u00e1r. 2017. Focal Loss for Dense Object Detection. In ICCV."},{"key":"e_1_2_1_51_1","doi-asserted-by":"crossref","unstructured":"Zachary Chase Lipton Charles Elkan and Balakrishnan Narayanaswamy. 2014. Optimal Thresholding of Classifiers to Maximize F1 Measure. In ECML-PKDD.  Zachary Chase Lipton Charles Elkan and Balakrishnan Narayanaswamy. 2014. Optimal Thresholding of Classifiers to Maximize F1 Measure. In ECML-PKDD.","DOI":"10.1007\/978-3-662-44851-9_15"},{"key":"e_1_2_1_52_1","volume-title":"Model-Based Synthetic Sampling for Imbalanced Data. TKDE","author":"Liu Chien-Liang","year":"2020","unstructured":"Chien-Liang Liu and Po-Yen Hsieh . 2020. Model-Based Synthetic Sampling for Imbalanced Data. TKDE ( 2020 ). Chien-Liang Liu and Po-Yen Hsieh. 2020. Model-Based Synthetic Sampling for Imbalanced Data. TKDE (2020)."},{"key":"e_1_2_1_53_1","unstructured":"Zhining Liu Wei Cao Zhifeng Gao Jiang Bian Hechang Chen Yi Chang and Tie-Yan Liu. 2020. Self-paced Ensemble for Highly Imbalanced Massive Data Classification. In ICDE.  Zhining Liu Wei Cao Zhifeng Gao Jiang Bian Hechang Chen Yi Chang and Tie-Yan Liu. 2020. Self-paced Ensemble for Highly Imbalanced Massive Data Classification. In ICDE."},{"key":"e_1_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-31635-8_237"},{"key":"e_1_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucli.2021.03.006"},{"key":"e_1_2_1_56_1","unstructured":"Pankaj Malhotra Vishnu TV Lovekesh Vig Puneet Agarwal and Gautam Shroff. 2017. TimeNet: Pre-trained deep recurrent neural network for time series classification. In ESANN.  Pankaj Malhotra Vishnu TV Lovekesh Vig Puneet Agarwal and Gautam Shroff. 2017. TimeNet: Pre-trained deep recurrent neural network for time series classification. In ESANN."},{"key":"e_1_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-55861-w"},{"key":"e_1_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1136\/jnnp-2013-305515"},{"key":"e_1_2_1_59_1","unstructured":"Aditya Menon Harikrishna Narasimhan Shivani Agarwal and Sanjay Chawla. 2013. On the statistical consistency of algorithms for binary classification under class imbalance. In ICML.  Aditya Menon Harikrishna Narasimhan Shivani Agarwal and Sanjay Chawla. 2013. On the statistical consistency of algorithms for binary classification under class imbalance. In ICML."},{"key":"e_1_2_1_60_1","unstructured":"Harikrishna Narasimhan Purushottam Kar and Prateek Jain. 2015. Optimizing Non-decomposable Performance Measures: A Tale of Two Classes. In ICML.  Harikrishna Narasimhan Purushottam Kar and Prateek Jain. 2015. Optimizing Non-decomposable Performance Measures: A Tale of Two Classes. In ICML."},{"key":"e_1_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jneumeth.2012.07.015"},{"key":"e_1_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1002\/ana.410370410"},{"key":"e_1_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1145\/2814710.2814719"},{"key":"e_1_2_1_64_1","volume-title":"Communications in Computer and Information Science (CCIS)","volume":"1197","author":"Palpanas Themis","year":"2020","unstructured":"Themis Palpanas . 2020 . Evolution of a Data Series Index - The iSAX Family of Data Series Indexes . In Communications in Computer and Information Science (CCIS) , Vol. 1197 . Themis Palpanas. 2020. Evolution of a Data Series Index - The iSAX Family of Data Series Indexes. In Communications in Computer and Information Science (CCIS), Vol. 1197."},{"key":"e_1_2_1_65_1","unstructured":"Shameem Puthiya Parambath Nicolas Usunier and Yves Grandvalet. 2014. Optimizing F-Measures by Cost-Sensitive Classification. In NIPS.  Shameem Puthiya Parambath Nicolas Usunier and Yves Grandvalet. 2014. Optimizing F-Measures by Cost-Sensitive Classification. In NIPS."},{"key":"e_1_2_1_66_1","volume-title":"Salvador Espa\u00f1a Boquera, and Mar\u00eda Jos\u00e9 Castro Bleda","author":"Pastor-Pellicer Joan","year":"2013","unstructured":"Joan Pastor-Pellicer , Francisco Zamora-Mart\u00ednez , Salvador Espa\u00f1a Boquera, and Mar\u00eda Jos\u00e9 Castro Bleda . 2013 . F-Measure as the Error Function to Train Neural Networks. In IWANN. Joan Pastor-Pellicer, Francisco Zamora-Mart\u00ednez, Salvador Espa\u00f1a Boquera, and Mar\u00eda Jos\u00e9 Castro Bleda. 2013. F-Measure as the Error Function to Train Neural Networks. In IWANN."},{"key":"e_1_2_1_67_1","volume-title":"ParIS: The Next Destination for Fast Data Series Indexing and Query Answering","author":"Peng Botao","year":"2018","unstructured":"Botao Peng , Panagiota Fatourou , and Themis Palpanas . 2018. ParIS: The Next Destination for Fast Data Series Indexing and Query Answering . IEEE BigData ( 2018 ). Botao Peng, Panagiota Fatourou, and Themis Palpanas. 2018. ParIS: The Next Destination for Fast Data Series Indexing and Query Answering. IEEE BigData (2018)."},{"key":"e_1_2_1_68_1","volume-title":"MESSI: In-Memory Data Series Indexing. In ICDE.","author":"Peng Botao","year":"2020","unstructured":"Botao Peng , Panagiota Fatourou , and Themis Palpanas . 2020 . MESSI: In-Memory Data Series Indexing. In ICDE. Botao Peng, Panagiota Fatourou, and Themis Palpanas. 2020. MESSI: In-Memory Data Series Indexing. In ICDE."},{"key":"e_1_2_1_69_1","volume-title":"Data Series Indexing on Multi-core Architectures. TKDE","author":"Peng Botao","year":"2020","unstructured":"Botao Peng , Panagiota Fatourou , and Themis Palpanas . 2020. ParIS+ : Data Series Indexing on Multi-core Architectures. TKDE ( 2020 ). Botao Peng, Panagiota Fatourou, and Themis Palpanas. 2020. ParIS+: Data Series Indexing on Multi-core Architectures. TKDE (2020)."},{"key":"e_1_2_1_70_1","article-title":"Fast data series indexing for in-memory data","volume":"30","author":"Peng Botao","year":"2021","unstructured":"Botao Peng , Panagiota Fatourou , and Themis Palpanas . 2021 . Fast data series indexing for in-memory data . VLDB J. 30 , 6 (2021). Botao Peng, Panagiota Fatourou, and Themis Palpanas. 2021. Fast data series indexing for in-memory data. VLDB J. 30, 6 (2021).","journal-title":"VLDB J."},{"key":"e_1_2_1_71_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE51399.2021.00171"},{"key":"e_1_2_1_72_1","unstructured":"Joseph Picone. 2015. Temple University EEG Corpus. https:\/\/isip.piconepress.com\/projects\/tuh_eeg\/html\/downloads.shtml. Accessed: 2022-11-18.  Joseph Picone. 2015. Temple University EEG Corpus. https:\/\/isip.piconepress.com\/projects\/tuh_eeg\/html\/downloads.shtml. Accessed: 2022-11-18."},{"key":"e_1_2_1_73_1","first-page":"114301","article-title":"DBIG-US: A two-stage under-sampling algorithm to face the class imbalance problem","volume":"168","author":"Ponce Ang\u00e9lica Guzm\u00e1n","year":"2021","unstructured":"Ang\u00e9lica Guzm\u00e1n Ponce , Jos\u00e9 Salvador S\u00e1nchez , Rosa Maria Valdovinos , and Jos\u00e9 Raymundo Marcial-Romero . 2021 . DBIG-US: A two-stage under-sampling algorithm to face the class imbalance problem . ESWA 168 (2021), 114301 . Ang\u00e9lica Guzm\u00e1n Ponce, Jos\u00e9 Salvador S\u00e1nchez, Rosa Maria Valdovinos, and Jos\u00e9 Raymundo Marcial-Romero. 2021. DBIG-US: A two-stage under-sampling algorithm to face the class imbalance problem. ESWA 168 (2021), 114301.","journal-title":"ESWA"},{"key":"e_1_2_1_74_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1474-4422(20)30396-3"},{"key":"e_1_2_1_75_1","unstructured":"Robert J Quon Stephen Meisenhelter Edward J Camp Markus E Testorf Yinchen Song Qingyuan Song George W Culler Payam Moein and Barbara C Jobst. 2021. Dartmouth ECoG Lab Automated Spike Detector. https:\/\/github.com\/ecoglab\/aied. Accessed: 2022-11-18.  Robert J Quon Stephen Meisenhelter Edward J Camp Markus E Testorf Yinchen Song Qingyuan Song George W Culler Payam Moein and Barbara C Jobst. 2021. Dartmouth ECoG Lab Automated Spike Detector. https:\/\/github.com\/ecoglab\/aied. Accessed: 2022-11-18."},{"key":"e_1_2_1_76_1","volume-title":"AiED: Artificial intelligence for the detection of intracranial interictal epileptiform discharges. Clinical Neurophysiology","author":"Quon Robert J","year":"2022","unstructured":"Robert J Quon , Stephen Meisenhelter , Edward J Camp , Markus E Testorf , Yinchen Song , Qingyuan Song , George W Culler , Payam Moein , and Barbara C Jobst . 2022. AiED: Artificial intelligence for the detection of intracranial interictal epileptiform discharges. Clinical Neurophysiology ( 2022 ). Robert J Quon, Stephen Meisenhelter, Edward J Camp, Markus E Testorf, Yinchen Song, Qingyuan Song, George W Culler, Payam Moein, and Barbara C Jobst. 2022. AiED: Artificial intelligence for the detection of intracranial interictal epileptiform discharges. Clinical Neurophysiology (2022)."},{"key":"e_1_2_1_77_1","volume-title":"Imputation-Based Ensemble Techniques for Class Imbalance Learning. TKDE","author":"Razavi-Far Roozbeh","year":"2021","unstructured":"Roozbeh Razavi-Far , Maryam Farajzadeh-Zanjani , Boyu Wang , Mehrdad Saif , and Shiladitya Chakrabarti . 2021. Imputation-Based Ensemble Techniques for Class Imbalance Learning. TKDE ( 2021 ). Roozbeh Razavi-Far, Maryam Farajzadeh-Zanjani, Boyu Wang, Mehrdad Saif, and Shiladitya Chakrabarti. 2021. Imputation-Based Ensemble Techniques for Class Imbalance Learning. TKDE (2021)."},{"key":"e_1_2_1_78_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.seizure.2021.12.012"},{"key":"e_1_2_1_79_1","volume-title":"Optimizing non-decomposable measures with deep networks. Machine Learning","author":"Sanyal Amartya","year":"2018","unstructured":"Amartya Sanyal , Pawan Kumar , Purushottam Kar , Sanjay Chawla , and Fabrizio Sebastiani . 2018. Optimizing non-decomposable measures with deep networks. Machine Learning ( 2018 ). Amartya Sanyal, Pawan Kumar, Purushottam Kar, Sanjay Chawla, and Fabrizio Sebastiani. 2018. Optimizing non-decomposable measures with deep networks. Machine Learning (2018)."},{"key":"e_1_2_1_80_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinph.2016.11.005"},{"key":"e_1_2_1_81_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1535-7511.2006.00145.x"},{"key":"e_1_2_1_82_1","doi-asserted-by":"publisher","DOI":"10.1017\/S0012162206000806"},{"key":"e_1_2_1_83_1","volume-title":"Minority Sub-Region Estimation-Based Oversampling for Imbalance Learning. TKDE","author":"Sun Yi","year":"2022","unstructured":"Yi Sun , Lijun Cai , Bo Liao , and Wen Zhu . 2022. Minority Sub-Region Estimation-Based Oversampling for Imbalance Learning. TKDE ( 2022 ). Yi Sun, Lijun Cai, Bo Liao, and Wen Zhu. 2022. Minority Sub-Region Estimation-Based Oversampling for Imbalance Learning. TKDE (2022)."},{"key":"e_1_2_1_84_1","volume-title":"Data Augmentation Using GANs. arXiv:1904.09135 [cs, stat] (April","author":"Kiyoiti dos Santos Tanaka Fabio Henrique","year":"2019","unstructured":"Fabio Henrique Kiyoiti dos Santos Tanaka and Claus Aranha . 2019. Data Augmentation Using GANs. arXiv:1904.09135 [cs, stat] (April 2019 ). arXiv:1904.09135 [cs, stat] Fabio Henrique Kiyoiti dos Santos Tanaka and Claus Aranha. 2019. Data Augmentation Using GANs. arXiv:1904.09135 [cs, stat] (April 2019). arXiv:1904.09135 [cs, stat]"},{"key":"e_1_2_1_85_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinph.2018.06.024"},{"key":"e_1_2_1_86_1","doi-asserted-by":"publisher","DOI":"10.1001\/archneur.1992.00530270045017"},{"key":"e_1_2_1_87_1","doi-asserted-by":"publisher","DOI":"10.1002\/epi4.12066"},{"key":"e_1_2_1_88_1","volume-title":"Foundation of evaluation. Journal of documentation","author":"Van Rijsbergen Cornelis Joost","year":"1974","unstructured":"Cornelis Joost Van Rijsbergen . 1974. Foundation of evaluation. Journal of documentation ( 1974 ). Cornelis Joost Van Rijsbergen. 1974. Foundation of evaluation. Journal of documentation (1974)."},{"key":"e_1_2_1_89_1","doi-asserted-by":"crossref","unstructured":"Qitong Wang and Themis Palpanas. 2021. Deep Learning Embeddings for Data Series Similarity Search. In KDD.  Qitong Wang and Themis Palpanas. 2021. Deep Learning Embeddings for Data Series Similarity Search. In KDD.","DOI":"10.1145\/3447548.3467317"},{"key":"e_1_2_1_90_1","volume-title":"Dumpy: A Compact and Adaptive Index for Large Data Series Collections. In SIGMOD.","author":"Wang Zeyu","year":"2023","unstructured":"Zeyu Wang , Qitong Wang , Peng Wang , Themis Palpanas , and Wei Wang . 2023 . Dumpy: A Compact and Adaptive Index for Large Data Series Collections. In SIGMOD. Zeyu Wang, Qitong Wang, Peng Wang, Themis Palpanas, and Wei Wang. 2023. Dumpy: A Compact and Adaptive Index for Large Data Series Collections. In SIGMOD."},{"key":"e_1_2_1_91_1","doi-asserted-by":"publisher","DOI":"10.1016\/0013-4694(93)90149-P"},{"key":"e_1_2_1_92_1","doi-asserted-by":"publisher","DOI":"10.1016\/0013-4694(95)00221-9"},{"key":"e_1_2_1_93_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1388-2457(02)00297-3"},{"key":"e_1_2_1_94_1","doi-asserted-by":"publisher","DOI":"10.4065\/77.9.991"},{"key":"e_1_2_1_95_1","volume-title":"Gaussian Distribution Based Oversampling for Imbalanced Data Classification. TKDE","author":"Xie Yuxi","year":"2022","unstructured":"Yuxi Xie , Min Qiu , Haibo Zhang , Lizhi Peng , and Zhenxiang Chen . 2022. Gaussian Distribution Based Oversampling for Imbalanced Data Classification. TKDE ( 2022 ). Yuxi Xie, Min Qiu, Haibo Zhang, Lizhi Peng, and Zhenxiang Chen. 2022. Gaussian Distribution Based Oversampling for Imbalanced Data Classification. TKDE (2022)."},{"key":"e_1_2_1_96_1","unstructured":"Bowei Yan Oluwasanmi Koyejo Kai Zhong and Pradeep Ravikumar. 2018. Binary Classification with Karmic Threshold-Quasi-Concave Metrics. In ICML.  Bowei Yan Oluwasanmi Koyejo Kai Zhong and Pradeep Ravikumar. 2018. Binary Classification with Karmic Threshold-Quasi-Concave Metrics. In ICML."},{"key":"e_1_2_1_97_1","doi-asserted-by":"crossref","unstructured":"Yan Yan Tianbao Yang Yi Yang and Jianhui Chen. 2017. A Framework of Online Learning with Imbalanced Streaming Data. In AAAI.  Yan Yan Tianbao Yang Yi Yang and Jianhui Chen. 2017. A Framework of Online Learning with Imbalanced Streaming Data. In AAAI.","DOI":"10.1609\/aaai.v31i1.10837"},{"key":"e_1_2_1_98_1","volume-title":"Wee Sun Lee, and Hai Leong Chieu.","author":"Ye Nan","year":"2012","unstructured":"Nan Ye , Kian Ming Chai , Wee Sun Lee, and Hai Leong Chieu. 2012 . Optimizing F-measures: a tale of two approaches. In ICML. Nan Ye, Kian Ming Chai, Wee Sun Lee, and Hai Leong Chieu. 2012. Optimizing F-measures: a tale of two approaches. In ICML."},{"key":"e_1_2_1_99_1","doi-asserted-by":"crossref","unstructured":"Jian Yin Chunjing Gan Kaiqi Zhao Xuan Lin Zhe Quan and Zhi-Jie Wang. 2020. A Novel Model for Imbalanced Data Classification. In AAAI.  Jian Yin Chunjing Gan Kaiqi Zhao Xuan Lin Zhe Quan and Zhi-Jie Wang. 2020. A Novel Model for Imbalanced Data Classification. In AAAI.","DOI":"10.1609\/aaai.v34i04.6145"},{"key":"e_1_2_1_100_1","unstructured":"Manzil Zaheer Satwik Kottur Amr Ahmed Jos\u00e9 Moura and Alex Smola. 2017. Canopy Fast Sampling with Cover Trees. In ICML.  Manzil Zaheer Satwik Kottur Amr Ahmed Jos\u00e9 Moura and Alex Smola. 2017. Canopy Fast Sampling with Cover Trees. In ICML."},{"key":"e_1_2_1_101_1","doi-asserted-by":"crossref","unstructured":"George Zerveas Srideepika Jayaraman Dhaval Patel Anuradha Bhamidipaty and Carsten Eickhoff. 2021. A Transformer-based Framework for Multivariate Time Series Representation Learning. In KDD.  George Zerveas Srideepika Jayaraman Dhaval Patel Anuradha Bhamidipaty and Carsten Eickhoff. 2021. A Transformer-based Framework for Multivariate Time Series Representation Learning. In KDD.","DOI":"10.1145\/3447548.3467401"},{"key":"e_1_2_1_102_1","unstructured":"Jingzhao Zhang Tianxing He Suvrit Sra and Ali Jadbabaie. 2020. Why Gradient Clipping Accelerates Training: A Theoretical Justification for Adaptivity. In ICLR.  Jingzhao Zhang Tianxing He Suvrit Sra and Ali Jadbabaie. 2020. Why Gradient Clipping Accelerates Training: A Theoretical Justification for Adaptivity. In ICLR."},{"key":"e_1_2_1_103_1","volume-title":"Adaptive Cost-Sensitive Online Classification. TKDE","author":"Zhao Peilin","year":"2019","unstructured":"Peilin Zhao , Yifan Zhang , Min Wu , Steven C. H. Hoi , Mingkui Tan , and Junzhou Huang . 2019. Adaptive Cost-Sensitive Online Classification. TKDE ( 2019 ). Peilin Zhao, Yifan Zhang, Min Wu, Steven C. H. Hoi, Mingkui Tan, and Junzhou Huang. 2019. Adaptive Cost-Sensitive Online Classification. TKDE (2019)."},{"key":"e_1_2_1_104_1","volume-title":"Training Cost-Sensitive Neural Networks with Methods Addressing the Class Imbalance Problem. TKDE","author":"Zhou Zhi-Hua","year":"2006","unstructured":"Zhi-Hua Zhou and Xu-Ying Liu . 2006. Training Cost-Sensitive Neural Networks with Methods Addressing the Class Imbalance Problem. TKDE ( 2006 ). Zhi-Hua Zhou and Xu-Ying Liu. 2006. Training Cost-Sensitive Neural Networks with Methods Addressing the Class Imbalance Problem. TKDE (2006)."},{"key":"e_1_2_1_105_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41582-019-0224-y"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3570690.3570698","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,23]],"date-time":"2023-01-23T17:35:12Z","timestamp":1674495312000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3570690.3570698"}},"subtitle":["A Deep Learning Framework for Detecting Highly Imbalanced Interictal Epileptiform Discharges"],"short-title":[],"issued":{"date-parts":[[2022,11]]},"references-count":105,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2022,11]]}},"alternative-id":["10.14778\/3570690.3570698"],"URL":"https:\/\/doi.org\/10.14778\/3570690.3570698","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2022,11]]},"assertion":[{"value":"2023-01-23","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}