Feature-embedding neural processes for missing value prediction결측값 예측을 위한 피처-임베딩 뉴럴 프로세스

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This paper addresses the missing value prediction problem in data. Existing missing value prediction studies apply their algorithms after replacing missing values with a constant value, which does not represent the missing values clearly and has disadvantages in processing data with a high missing rate. Also, it is needed to consider the uncertainty in missing value prediction which is important for model reliability. We propose a model that generalizes a single data sample from its observations and predicts the distribution of the value of a query feature to measure uncertainty. Our model can deal with an incomplete data sample independently of missing values, and the prediction uncertainty can be measured by the output variance. In this process, the feature embedding matrix and feature importance weights are introduced to represent each feature and learned jointly with the proposed model. Through experiments in various aspects, we verify that the proposed model effectively predicts the missing values of data and offers reliable uncertainty measures.
Advisors
Choi, Ho-Jinresearcher최호진researcher
Description
한국과학기술원 :전산학부,
Publisher
한국과학기술원
Issue Date
2020
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 전산학부, 2020.2,[iii, 28 p. :]

Keywords

missing value prediction▼auncertainty measure▼aneural processes▼adeep learning▼afeature; 결측값 예측▼a뉴럴 프로세스▼a불확실성 측정▼a심층 학습▼a특징

URI
http://hdl.handle.net/10203/284655
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=910971&flag=dissertation
Appears in Collection
CS-Theses_Master(석사논문)
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