Abstract
In order to enhance the performance, rare class prediction are to need the feature selection method for target class-related feature. Traditional data mining algorithms fail to predict rare class, as the class imbalanced data models are inherently built in favor of the majority of class-common characteristics among data instances. In the present paper, we propose the Euclidean distance- and standard deviation-based feature selection and over-sampling for the fault detection prediction model. We study applying the semiconductor manufacturing process control in fault detection prediction. First, the features calculate the MAV (Mean Absolute Value) median values. Secondly, the MeanEuSTDEV (the mean of Euclidean distance and standard deviation) are used to select the most appropriate features of the classification model. Third, to address the rare class over-fitting problem, oversampling is used. Finally, learning generates the fault detection prediction data-mining model. Furthermore, the prediction model is applied to measure the performance.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Chomboon, K., Kerdprasop, K., Kerdprasop, N.: Rare class discovery techniques for highly imbalance data. In Proceeding International Multi Conference of Engineers and Computer Scientists, vol. 1 (2013)
May, G.S., Spanos, C.J.: Fundamentals of Semiconductor Manufacturing and Process Control. Wiley, New York (2006)
Purnomo, M.R.A., Dewi, I.H.S.: A manufacturing quality assessment model based-on two stages interval type-2 fuzzy logic. In: IOP Conference Series: Materials Science and Engineering, vol. 105, no. 1, pp. 012044. IOP Publishing (2016)
Arif, F., Suryana, N., Hussin, B.: Cascade quality prediction method using multiple PCA+ID3 for multi-stage manufacturing system. IERI Procedia 4, 201–207 (2013)
SEmi COnductor Manufacturing (2010). http://www.causality.inf.ethz.ch/repository.php
Phinyomark, A., Hirunviriya, S., Limsakul, C., Phukpattaranont, P.: Evaluation of EMG feature extraction for hand movement recognition based on Euclidean distance and standard deviation. In: International Conference on IEEE (ECTI-CON), pp. 856–860 (2010)
Acknowledgement
This work was funded by the Ministry of Science, ICT and Future Planning (NRF-2015R1C1A2A01051452).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Kim, J.K., Cho, K.C., Lee, J.S., Han, Y.S. (2017). Feature Selection Techniques for Improving Rare Class Classification in Semiconductor Manufacturing Process. In: Jung, J., Kim, P. (eds) Big Data Technologies and Applications. BDTA 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 194. Springer, Cham. https://doi.org/10.1007/978-3-319-58967-1_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-58967-1_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-58966-4
Online ISBN: 978-3-319-58967-1
eBook Packages: Computer ScienceComputer Science (R0)