Imputing manufacturing material in data mining

Ruey Ling Yeh, Ching Liu, Ben Chang Shia, Yu Ting Cheng, Ya Fang Huwang

研究成果: 雜誌貢獻文章

4 引文 斯高帕斯(Scopus)

摘要

Data plays a vital role as a source of information to organizations, especially in times of information and technology. One encounters a not-so-perfect database from which data is missing, and the results obtained from such a database may provide biased or misleading solutions. Therefore, imputing missing data to a database has been regarded as one of the major steps in data mining. The present research used different methods of data mining to construct imputative models in accordance with different types of missing data. When the missing data is continuous, regression models and Neural Networks are used to build imputative models. For the categorical missing data, the logistic regression model, neural network, C5.0 and CART are employed to construct imputative models. The results showed that the regression model was found to provide the best estimate of continuous missing data; but for categorical missing data, the C5.0 model proved the best method.

原文英語
頁(從 - 到)109-118
頁數10
期刊Journal of Intelligent Manufacturing
19
發行號1
DOIs
出版狀態已發佈 - 二月 2008
對外發佈Yes

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Artificial Intelligence

指紋 深入研究「Imputing manufacturing material in data mining」主題。共同形成了獨特的指紋。

  • 引用此

    Yeh, R. L., Liu, C., Shia, B. C., Cheng, Y. T., & Huwang, Y. F. (2008). Imputing manufacturing material in data mining. Journal of Intelligent Manufacturing, 19(1), 109-118. https://doi.org/10.1007/s10845-007-0067-z