Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 권보경 | - |
dc.date.accessioned | 2022-05-23T06:51:38Z | - |
dc.date.available | 2022-05-23T06:51:38Z | - |
dc.date.issued | 2022-02 | - |
dc.identifier.citation | COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION; FEB 3 2022, 15p. | en_US |
dc.identifier.issn | 03610918 | - |
dc.identifier.issn | 15324141 | - |
dc.identifier.uri | https://www.tandfonline.com/doi/full/10.1080/03610918.2022.2037636 | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/171066 | - |
dc.description.abstract | Modern computing and communication technologies can make data collection procedures very efficient, while our ability to analyze and extract information from large data sets is hard-pressed to keep up with our capacity for data collection. If we are interested in learning a classification/prediction rule that was not considered in the original data collection procedure, then there is usually a lack of label information in the original data. Thus, how to start with minimum number of labeled data and aggressively selected most informative samples for being labeled becomes an important issue. The thoughts of active learning, with subjects to be selected sequentially without using label information, is a possible outlet for this situation. In addition, if we can identify variables, from the lengthy variable list of the data set, for constructing an effective classification rule with good interpretation ability will be better. Here, we propose an active learning procedure targeted the maximum area under the receiver operating characteristic curve via a commonly use logistic model, which can sequentially select the effective informative subjects and variables, simultaneously. We discuss the asymptotic properties of the proposed procedure and illustrate it with some synthesized and real data sets. | en_US |
dc.description.sponsorship | A part of this research is supported by the National Natural Science Foundation of China (No. 11971457), the Anhui Provincial Natural Science Foundation (No. 1908085MA06), Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No. 2021-0-01343, Artificial Intelligence Convergence Research Center (Hanyang University ERICA), 2021), and the Ministry of Science and Technology, Taiwan (MOST 108-2118-M-001-004-MY3). A part of this research is supported by the National Natural Science Foundation of China (No. 11971457), the Anhui Provincial Natural Science Foundation (No. 1908085MA06), Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No. 2021-0-01343, Artificial Intelligence Convergence Research Center (Hanyang University ERICA), 2021), and the Ministry of Science and Technology, Taiwan (MOST 108-2118-M-001-004-MY3). | en_US |
dc.language.iso | en | en_US |
dc.publisher | TAYLOR & FRANCIS INC | en_US |
dc.subject | Active learning | en_US |
dc.subject | Area under ROC curve | en_US |
dc.subject | Classification | en_US |
dc.subject | Local optimal design | en_US |
dc.subject | Shrinkage estimate | en_US |
dc.subject | Stopping time | en_US |
dc.title | Active learning with logistic models featuring simultaneous variable and subject selection | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1080/03610918.2022.2037636 | - |
dc.relation.page | 1-15 | - |
dc.relation.journal | COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION | - |
dc.contributor.googleauthor | Wang, Zhanfeng | - |
dc.contributor.googleauthor | Kwon, Amy M. | - |
dc.contributor.googleauthor | Chang, Yuan-chin Ivan | - |
dc.relation.code | 2022039288 | - |
dc.sector.campus | E | - |
dc.sector.daehak | [E] | - |
dc.identifier.pid | amykwon | - |
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