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Original Articles

Supervised learning for change-point detection

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Pages 2853-2868 | Published online: 22 Feb 2007

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Karim Atashgar & Mahnaz Boush. (2023) A convolutional neural network to identify the change point of a multistage process profile with cascade property. International Journal of Quality & Reliability Management.
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Baruc E. Pérez-Benítez, Víctor G. Tercero-Gómez & Marzieh Khakifirooz. (2023) A Review on Statistical Process Control in Healthcare: Data-Driven Monitoring Schemes. IEEE Access 11, pages 56248-56272.
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Mine Kerpicci, Milos Prvulovic & Alenka Zajic. (2022) A Hierarchical Approach for Multiple Periodicity Detection in Software Code Analysis. IEEE Access 10, pages 106936-106945.
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Bernadin Namoano, Andrew Starr, Christos Emmanouilidis & Ruiz Carcel Cristobal. (2019) Online change detection techniques in time series: An overview. Online change detection techniques in time series: An overview.
Wook‐Yeon Hwang & Jong‐Seok Lee. (2013) Shifting artificial data to detect system failures. International Transactions in Operational Research 22:2, pages 363-378.
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Amirhossein Amiri, S. T. A. Niaki & Alireza Taheri Moghadam. (2014) A probabilistic artificial neural network-based procedure for variance change point estimation. Soft Computing 19:3, pages 691-700.
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Farzaneh Ahmadzadeh, Jan Lundberg & Thomas Strömberg. (2013) Multivariate process parameter change identification by neural network. The International Journal of Advanced Manufacturing Technology 69:9-12, pages 2261-2268.
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Karim Atashgar. (2012) Identification of the change point: an overview. The International Journal of Advanced Manufacturing Technology 64:9-12, pages 1663-1683.
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Rassoul Noorossana, Karim Atashgar & Abbas Saghaei. (2011) An integrated supervised learning solution for monitoring process mean vector. The International Journal of Advanced Manufacturing Technology 56:5-8, pages 755-765.
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Karim Atashgar & R. Noorossana. (2010) An integrating approach to root cause analysis of a bivariate mean vector with a linear trend disturbance. The International Journal of Advanced Manufacturing Technology 52:1-4, pages 407-420.
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Hung‐Fan Chang & Chih‐Ming Luo. (2010) Analyze innovation strategy of technical‐intensive industries: scenario analysis viewpoint. Business Strategy Series 11:5, pages 302-307.
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C. M. Luo, Y. Kuo & Y.C. Huang. (2010) Applying scenario technology analysis for new product dynamic strategic planning. Applying scenario technology analysis for new product dynamic strategic planning.
Hung‐Fan Chang. (2010) Analysis of the innovation strategy of technical‐intensive industries: scenario analysis viewpoint. Business Strategy Series 11:3, pages 152-157.
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Albert Y. Kim, Caren Marzban, Donald B. Percival & Werner Stuetzle. (2009) Using labeled data to evaluate change detectors in a multivariate streaming environment. Signal Processing 89:12, pages 2529-2536.
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Liliana Argotte, Manuel Mejia-Lavalle & Ricardo Sosa. (2009) Business Intelligence and Energy Markets: A Survey. Business Intelligence and Energy Markets: A Survey.
A. K. Choudhary, J. A. Harding & M. K. Tiwari. (2008) Data mining in manufacturing: a review based on the kind of knowledge. Journal of Intelligent Manufacturing 20:5, pages 501-521.
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Andrea Johnston, Blair F. Johnston, Alan R. Kennedy & Alastair J. Florence. (2008) Targeted crystallisation of novel carbamazepine solvates based on a retrospective Random Forest classification. CrystEngComm 10:1, pages 23-25.
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Belmiro P.M. Duarte & P.M. Saraiva. 2008. 18th European Symposium on Computer Aided Process Engineering. 18th European Symposium on Computer Aided Process Engineering 665 670 .

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