https://scholars.lib.ntu.edu.tw/handle/123456789/607517
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.author | Wang B | en_US |
dc.contributor.author | Jiang L | en_US |
dc.contributor.author | Zhu W | en_US |
dc.contributor.author | Guo L | en_US |
dc.contributor.author | Chen J | en_US |
dc.contributor.author | YAO-WEN CHANG | - |
dc.creator | Wang B;Jiang L;Zhu W;Guo L;Chen J;Chang Y.-W. | - |
dc.date.accessioned | 2022-04-25T06:44:15Z | - |
dc.date.available | 2022-04-25T06:44:15Z | - |
dc.date.issued | 2021 | - |
dc.identifier.issn | 0738100X | - |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119418184&doi=10.1109%2fDAC18074.2021.9586237&partnerID=40&md5=66c4d9968c52c5030ea13d05552764f6 | - |
dc.identifier.uri | https://scholars.lib.ntu.edu.tw/handle/123456789/607517 | - |
dc.description.abstract | The data imbalance problem often occurs in nanometer VLSI applications, where normal cases far outnumber error ones. Many imbalanced data handling methods have been proposed, such as oversampling minority class samples and downsampling majority class samples. However, existing methods focus on improving the quality of minority classes while causing quality deterioration of majority ones. In this paper, we propose a two-stage classifier to handle the data imbalance problem. We first develop an iterative neural network framework to reduce false alarms. Then the oversampling method on a final classification network is applied to predict the two classes better. As a result, the data imbalance problem is well handled, and the quality deterioration of majority classes is also reduced. Since the iterative stage does not change any existing network structure, any convolutional neural network can be used in the framework. Compared with the state-of-the-art imbalanced data handling methods, experimental results on the hotspot detection problem show that our two-stage classification method achieves the best prediction accuracy and reduces false alarms significantly. ? 2021 IEEE. | - |
dc.relation.ispartof | Proceedings - Design Automation Conference | - |
dc.subject | Classification (of information) | - |
dc.subject | Convolutional neural networks | - |
dc.subject | Data handling | - |
dc.subject | Deterioration | - |
dc.subject | Iterative methods | - |
dc.subject | Data imbalance | - |
dc.subject | Down sampling | - |
dc.subject | Hotspot detections | - |
dc.subject | Imbalance problem | - |
dc.subject | Imbalanced data | - |
dc.subject | Nanometer VLSI | - |
dc.subject | Neural networks classifiers | - |
dc.subject | Over sampling | - |
dc.subject | Quality deteriorations | - |
dc.subject | Two-stage classifiers | - |
dc.subject | Errors | - |
dc.title | Two-Stage Neural Network Classifier for the Data Imbalance Problem with Application to Hotspot Detection | en_US |
dc.type | conference paper | en |
dc.identifier.doi | 10.1109/DAC18074.2021.9586237 | - |
dc.identifier.scopus | 2-s2.0-85119418184 | - |
dc.relation.pages | 175-180 | - |
dc.relation.journalvolume | 2021-December | - |
item.openairecristype | http://purl.org/coar/resource_type/c_5794 | - |
item.openairetype | conference paper | - |
item.grantfulltext | none | - |
item.cerifentitytype | Publications | - |
item.fulltext | no fulltext | - |
crisitem.author.dept | Electronics Engineering | - |
crisitem.author.dept | Electrical Engineering | - |
crisitem.author.dept | Computer Science and Information Engineering | - |
crisitem.author.dept | Center for Information and Electronics Technologies | - |
crisitem.author.dept | MediaTek-NTU Research Center | - |
crisitem.author.orcid | 0000-0002-0564-5719 | - |
crisitem.author.parentorg | College of Electrical Engineering and Computer Science | - |
crisitem.author.parentorg | College of Electrical Engineering and Computer Science | - |
crisitem.author.parentorg | College of Electrical Engineering and Computer Science | - |
crisitem.author.parentorg | Others: University-Level Research Centers | - |
crisitem.author.parentorg | Others: University-Level Research Centers | - |
顯示於: | 電信工程學研究所 |
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