DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | 이주호 | - |
dc.contributor.author | Kim, Seunghyun | - |
dc.contributor.author | 김승현 | - |
dc.date.accessioned | 2024-07-30T19:30:38Z | - |
dc.date.available | 2024-07-30T19:30:38Z | - |
dc.date.issued | 2024 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=1096059&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/321354 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 김재철AI대학원, 2024.2,[iii, 19 p. :] | - |
dc.description.abstract | a deep generative model for missing value imputation and a classifier. Extending the existing deep generative models to better capture structures of time-series data, our deep generative model part is trained to impute the missing values in multiple plausible ways, effectively modeling the uncertainty of the imputation. The classifier part takes the time series data along with the imputed missing values and classifies signals, and is trained to capture the predictive uncertainty due to the multiple possibilities of imputations. Importantly, we show that na ̈ıvely combining the generative model and the classifier could result in trivial solutions where the generative model does not produce meaningful imputations. To resolve this, we present a novel regularization technique that can promote the model to produce useful imputation values that help classification. Through extensive experiments on real-world time series data with missing values, we demonstrate the effectiveness of our method. | - |
dc.description.abstract | Multivariate time series data for real-world applications typically contain a significant amount of missing values. The dominant approach for classification with such missing values is to impute them heuristically with specific values (zero, mean, values of adjacent time-steps) or learnable parameters. However, these simple strategies do not take the data generative process into account, and more importantly, do not effectively capture the uncertainty in prediction due to the multiple possibilities for the missing values. In this paper, we propose a novel probabilistic framework for classification with multivariate time series data with missing values. Our model consists of two parts | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | 시계열 분류▼a결측치▼a확률 모델▼a불확실성 측정 | - |
dc.subject | Time series classification▼aMissing data▼aProbabilistic methods▼aUncertainty quantification | - |
dc.title | Probabilistic imputation for time-series classification with missing data | - |
dc.title.alternative | 결측치가 존재하는 시계열 데이터 분류를 위한 확률적 대체 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :김재철AI대학원, | - |
dc.contributor.alternativeauthor | Lee, Juho | - |
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