Bayesian Nonparametric Classification for Incomplete Data With a High Missing Rate: an Application to Semiconductor Manufacturing Data-Reference-Cited by-同舟云学术

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Bayesian Nonparametric Classification for Incomplete Data With a High Missing Rate: an Application to Semiconductor Manufacturing Data

Published:2023-05 Issue:2 Volume:36 Page:170-179
ISSN:0894-6507
Container-title:IEEE Transactions on Semiconductor Manufacturing
language:
Short-container-title:IEEE Trans. Semicond. Manufact.

Author:

Park Sewon¹^ORCID,Lee Kyeongwon²^ORCID,Jeong Da-Eun³,Ko Heung-Kook³,Lee Jaeyong²^ORCID

Affiliation:

1. Sewon Park is the Security Algorithm Lab, Samsung SDS, Seoul, Republic of Korea

2. Department of Statistics, Seoul National University, Seoul, Republic of Korea

3. Manufacturing Quality and Reliability Group, Samsung Electronics, Yongin, Republic of Korea

Funder

Samsung Electronics Company Ltd

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Industrial and Manufacturing Engineering,Condensed Matter Physics,Electronic, Optical and Magnetic Materials

Link

http://xplorestaging.ieee.org/ielx7/66/10119187/10050554.pdf?arnumber=10050554

Reference69 articles.

1. Dealing with missing values in neural network-based diagnostic systems

2. A constructive definition of Dirichlet priors;sethuraman;Statistica Sinica,1994

3. Improving cluster-based missing value estimation of DNA microarray data

4. Missing value imputation on missing completely at random data using multilayer perceptrons

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