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  • 2020
  • 2019
  • 2016

Author

  • George Runger
Article

Discovering the Nature of Variation in Nonlinear Profile Data

Shi, Z., Apley, D. W. & Runger, G., Jul 2 2016, In : Technometrics. 58, 3, p. 371-382 12 p.

Research output: Contribution to journalArticle

5 Scopus citations

Distinct Variation Pattern Discovery Using Alternating Nonlinear Principal Component Analysis

Howard, P., Apley, D. W. & Runger, G., Oct 26 2016, (Accepted/In press) In : IEEE Transactions on Neural Networks and Learning Systems.

Research output: Contribution to journalArticle

2 Scopus citations

Dynamic incorporation of prior knowledge from multiple domains in biomarker discovery

Guan, X., Runger, G. & Liu, L., Mar 11 2020, In : BMC bioinformatics. 21, 77.

Research output: Contribution to journalArticle

Open Access
1 Scopus citations

Identifying and visualizing part-to-part variation with spatially dense optical dimensional metrology data

Shi, Z., Apley, D. W. & Runger, G., Jan 1 2019, In : Journal of Quality Technology. 51, 1, p. 3-20 18 p.

Research output: Contribution to journalArticle

Interpretable regularized class association rules algorithm for classification in a categorical data space

Azmi, M., Runger, G. C. & Berrado, A., May 2019, In : Information Sciences. 483, p. 313-331 19 p.

Research output: Contribution to journalArticle

7 Scopus citations

Matched Forest: Supervised learning for high-dimensional matched case-control studies

Shomal Zadeh, N., Lin, S., Runger, G. C. & Wren, J., Mar 1 2020, In : Bioinformatics. 36, 5, p. 1570-1576 7 p.

Research output: Contribution to journalArticle

Monitoring temporal homogeneity in attributed network streams

Azarnoush, B., Paynabar, K., Bekki, J. & Runger, G., Jan 1 2016, In : Journal of Quality Technology. 48, 1, p. 28-43 16 p.

Research output: Contribution to journalArticle

33 Scopus citations

Multivariate bounded process adjustment schemes

Govind, N., del Castillo, E., Runger, G. & Janakiram, M., Jul 17 2016, (Accepted/In press) In : Quality Technology and Quantitative Management. p. 1-21 21 p.

Research output: Contribution to journalArticle

Predictive modeling using a nationally representative database to identify patients at risk of developing microalbuminuria

Villa-Zapata, L., Warholak, T., Slack, M., Malone, D., Murcko, A., Runger, G. & Levengood, M., Feb 1 2016, In : International Urology and Nephrology. 48, 2, p. 249-256 8 p.

Research output: Contribution to journalArticle

Projection-free kernel principal component analysis for denoising

Bui, A. T., Im, J. K., Apley, D. W. & Runger, G., Sep 10 2019, In : Neurocomputing. 357, p. 163-176 14 p.

Research output: Contribution to journalArticle

2 Scopus citations

Time series representation and similarity based on local autopatterns

Baydogan, M. G. & Runger, G., Mar 1 2016, In : Data Mining and Knowledge Discovery. 30, 2, p. 476-509 34 p.

Research output: Contribution to journalArticle

41 Scopus citations
Chapter

EEG-based user performance prediction using random forest in a dynamic learning environment

Lujan-Moreno, G. A., Atkinson, R. & Runger, G., Jan 1 2016, Intelligent Tutoring Systems: Structure, Applications and Challenges. Nova Science Publishers, Inc., p. 105-128 24 p.

Research output: Chapter in Book/Report/Conference proceedingChapter

Comment/debate

Rejoinder on: “On active learning methods for manifold data”

Li, H., Del Castillo, E. & Runger, G., Mar 1 2020, In : Test. 29, 1, p. 42-49 8 p.

Research output: Contribution to journalComment/debate

Conference contribution

Automated data mining methods for identifying energy efficiency opportunities using whole-building electricity data

Howard, P., Runger, G., Reddy, T. A. & Katipamula, S., 2016, ASHRAE Transactions - ASHRAE Winter Conference. Amer. Soc. Heating, Ref. Air-Conditoning Eng. Inc., Vol. 122. p. 422-433 12 p.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations