Online learning of the cause-and-effect knowledge of a manufacturing process

Jun-Geol Baek, Chang O. Kim, Sung Shick Kim

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

This paper deals with the intelligent learning of the cause-and-effect knowledge of a manufacturing process in online mode. This knowledge discovery problem is characterized as online learning where the knowledge is gradually found using the instances periodically obtained from the part processing of the process. We develop a new decision tree learning method called 'Statistical Batch based Decision tree Learning' (SBDL). To deal with large number of instances collected from the process, the concept of batch-based learning is introduced. A two-phased fitness test is also developed for measuring the fitness of the decision tree, thereby detecting the update point in time of the decision tree. The performance of SBDL has been verified with a real instance set collected from a Korean TFT-LCD manufacturing company.

Original languageEnglish
Pages (from-to)3275-3290
Number of pages16
JournalInternational Journal of Production Research
Volume40
Issue number14
DOIs
Publication statusPublished - 2002 Sep 20
Externally publishedYes

Fingerprint

Decision trees
Liquid crystal displays
Data mining
Statistical methods
Online learning
Manufacturing process
Decision tree
Processing
Batch
Industry
Fitness

ASJC Scopus subject areas

  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

Cite this

Online learning of the cause-and-effect knowledge of a manufacturing process. / Baek, Jun-Geol; Kim, Chang O.; Kim, Sung Shick.

In: International Journal of Production Research, Vol. 40, No. 14, 20.09.2002, p. 3275-3290.

Research output: Contribution to journalArticle

@article{1b1eddb146e64741a4a7e64b95e9c6e0,
title = "Online learning of the cause-and-effect knowledge of a manufacturing process",
abstract = "This paper deals with the intelligent learning of the cause-and-effect knowledge of a manufacturing process in online mode. This knowledge discovery problem is characterized as online learning where the knowledge is gradually found using the instances periodically obtained from the part processing of the process. We develop a new decision tree learning method called 'Statistical Batch based Decision tree Learning' (SBDL). To deal with large number of instances collected from the process, the concept of batch-based learning is introduced. A two-phased fitness test is also developed for measuring the fitness of the decision tree, thereby detecting the update point in time of the decision tree. The performance of SBDL has been verified with a real instance set collected from a Korean TFT-LCD manufacturing company.",
author = "Jun-Geol Baek and Kim, {Chang O.} and Kim, {Sung Shick}",
year = "2002",
month = "9",
day = "20",
doi = "10.1080/00207540210146921",
language = "English",
volume = "40",
pages = "3275--3290",
journal = "International Journal of Production Research",
issn = "0020-7543",
publisher = "Taylor and Francis Ltd.",
number = "14",

}

TY - JOUR

T1 - Online learning of the cause-and-effect knowledge of a manufacturing process

AU - Baek, Jun-Geol

AU - Kim, Chang O.

AU - Kim, Sung Shick

PY - 2002/9/20

Y1 - 2002/9/20

N2 - This paper deals with the intelligent learning of the cause-and-effect knowledge of a manufacturing process in online mode. This knowledge discovery problem is characterized as online learning where the knowledge is gradually found using the instances periodically obtained from the part processing of the process. We develop a new decision tree learning method called 'Statistical Batch based Decision tree Learning' (SBDL). To deal with large number of instances collected from the process, the concept of batch-based learning is introduced. A two-phased fitness test is also developed for measuring the fitness of the decision tree, thereby detecting the update point in time of the decision tree. The performance of SBDL has been verified with a real instance set collected from a Korean TFT-LCD manufacturing company.

AB - This paper deals with the intelligent learning of the cause-and-effect knowledge of a manufacturing process in online mode. This knowledge discovery problem is characterized as online learning where the knowledge is gradually found using the instances periodically obtained from the part processing of the process. We develop a new decision tree learning method called 'Statistical Batch based Decision tree Learning' (SBDL). To deal with large number of instances collected from the process, the concept of batch-based learning is introduced. A two-phased fitness test is also developed for measuring the fitness of the decision tree, thereby detecting the update point in time of the decision tree. The performance of SBDL has been verified with a real instance set collected from a Korean TFT-LCD manufacturing company.

UR - http://www.scopus.com/inward/record.url?scp=0037144833&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0037144833&partnerID=8YFLogxK

U2 - 10.1080/00207540210146921

DO - 10.1080/00207540210146921

M3 - Article

VL - 40

SP - 3275

EP - 3290

JO - International Journal of Production Research

JF - International Journal of Production Research

SN - 0020-7543

IS - 14

ER -