publication . Preprint . Article . 2018

Generic adaptation strategies for automated machine learning

Bogdan Gabrys;
Open Access English
  • Published: 27 Dec 2018
Abstract
Automation of machine learning model development is increasingly becoming an established research area. While automated model selection and automated data pre-processing have been studied in depth, there is, however, a gap concerning automated model adaptation strategies when multiple strategies are available. Manually developing an adaptation strategy, including estimation of relevant parameters can be time consuming and costly. In this paper we address this issue by proposing generic adaptation strategies based on approaches from earlier works. Experimental results after using the proposed strategies with three adaptive algorithms on 36 datasets confirm their ...
Subjects
free text keywords: Computer Science - Machine Learning, Statistics - Machine Learning
75 references, page 1 of 5

Alcobe JR (2004) Incremental Hill-Climbing Search Applied to Bayesian Network Structure Learning. In: Proceedings of the Eighth European Conference on Principles and Practice of Knowledge Discovery in Databases, Volume 3202 of Lecture Notes in Computer Science. Springer Verlag

Alippi C, Boracchi G, Roveri M (2012) Just-in-time ensemble of classi ers. In: The 2012 International Joint Conference on Neural Networks (IJCNN), IEEE, pp 1{8, DOI 10.1109/IJCNN.2012.6252540 [OpenAIRE]

Anderson E (1936) The Species Problem in Iris. Annals of the Missouri Botanical Garden 23(3):457, DOI 10.2307/2394164 [OpenAIRE]

Ba J, Frey B (2013) Adaptive dropout for training deep neural networks. In: NIPS'13 Proceedings of the 26th International Conference on Neural Information Processing Systems, pp 3084{3092

Bach SH, Maloof MA (2008) Paired Learners for Concept Drift. 2008 Eighth IEEE International Conference on Data Mining pp 23{32, DOI 10.1109/ICDM.2008. 119

Bakirov R, Gabrys B (2013) Investigation of Expert Addition Criteria for Dynamically Changing Online Ensemble Classi ers with Multiple Adaptive Mechanisms. In: Papadopoulos H, Andreou A, Iliadis L, Maglogiannis I (eds) Arti cial Intelligence Applications and Innovations, pp 646{656 [OpenAIRE]

Bakirov R, Gabrys B, Fay D (2015) On sequences of di erent adaptive mechanisms in non-stationary regression problems. In: 2015 International Joint Conference on Neural Networks (IJCNN), pp 1{8, DOI 10.1109/IJCNN.2015.7280779

Bakirov R, Gabrys B, Fay D (2016) Augmenting adaptation with retrospective model correction for non-stationary regression problems. In: 2016 International Joint Conference on Neural Networks (IJCNN), IEEE, pp 771{779, DOI 10. 1109/IJCNN.2016.7727278 [OpenAIRE]

Bakirov R, Gabrys B, Fay D (2017a) Multiple adaptive mechanisms for datadriven soft sensors. Computers & Chemical Engineering 96:42{54, DOI 10.1016/ j.compchemeng.2016.08.017 [OpenAIRE]

Bakirov R, Gabrys B, Fay D (2017b) Multiple adaptive mechanisms for datadriven soft sensors. Computers and Chemical Engineering 96, DOI 10.1016/j. compchemeng.2016.08.017 [OpenAIRE]

Basak J (2006) Online adaptive decision trees: Pattern classi cation and function approximation. Neural computation 18(9):2062{2101

Bifet A, Holmes G, Gavalda R, Pfahringer B, Kirkby R (2009) New Ensemble Methods For Evolving Data Streams. Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '09 pp 139{147, DOI 10.1145/1557019.1557041

Carpenter G, Grossberg S, Reynolds J (1991) ARTMAP: Supervised real-time learning and classi cation of nonstationary data by a self-organizing neural network. Neural networks 4:565{588

Castillo G, Gama J (2006) An Adaptive Prequential Learning Framework for Bayesian Network Classi ers. In: Furnkranz J, Sche er T, Spiliopoulou M (eds) Knowledge Discovery in Databases: PKDD 2006, Springer Berlin Heidelberg, Berlin, Heidelberg, Lecture Notes in Computer Science, vol 4213, pp 67{78, DOI 10.1007/11871637

Chen Y, Keogh E, Hu B, Begum N, Bagnall A, Mueen A, Batista G (2015) The UCR Time Series Classi cation Archive

75 references, page 1 of 5
Abstract
Automation of machine learning model development is increasingly becoming an established research area. While automated model selection and automated data pre-processing have been studied in depth, there is, however, a gap concerning automated model adaptation strategies when multiple strategies are available. Manually developing an adaptation strategy, including estimation of relevant parameters can be time consuming and costly. In this paper we address this issue by proposing generic adaptation strategies based on approaches from earlier works. Experimental results after using the proposed strategies with three adaptive algorithms on 36 datasets confirm their ...
Subjects
free text keywords: Computer Science - Machine Learning, Statistics - Machine Learning
75 references, page 1 of 5

Alcobe JR (2004) Incremental Hill-Climbing Search Applied to Bayesian Network Structure Learning. In: Proceedings of the Eighth European Conference on Principles and Practice of Knowledge Discovery in Databases, Volume 3202 of Lecture Notes in Computer Science. Springer Verlag

Alippi C, Boracchi G, Roveri M (2012) Just-in-time ensemble of classi ers. In: The 2012 International Joint Conference on Neural Networks (IJCNN), IEEE, pp 1{8, DOI 10.1109/IJCNN.2012.6252540 [OpenAIRE]

Anderson E (1936) The Species Problem in Iris. Annals of the Missouri Botanical Garden 23(3):457, DOI 10.2307/2394164 [OpenAIRE]

Ba J, Frey B (2013) Adaptive dropout for training deep neural networks. In: NIPS'13 Proceedings of the 26th International Conference on Neural Information Processing Systems, pp 3084{3092

Bach SH, Maloof MA (2008) Paired Learners for Concept Drift. 2008 Eighth IEEE International Conference on Data Mining pp 23{32, DOI 10.1109/ICDM.2008. 119

Bakirov R, Gabrys B (2013) Investigation of Expert Addition Criteria for Dynamically Changing Online Ensemble Classi ers with Multiple Adaptive Mechanisms. In: Papadopoulos H, Andreou A, Iliadis L, Maglogiannis I (eds) Arti cial Intelligence Applications and Innovations, pp 646{656 [OpenAIRE]

Bakirov R, Gabrys B, Fay D (2015) On sequences of di erent adaptive mechanisms in non-stationary regression problems. In: 2015 International Joint Conference on Neural Networks (IJCNN), pp 1{8, DOI 10.1109/IJCNN.2015.7280779

Bakirov R, Gabrys B, Fay D (2016) Augmenting adaptation with retrospective model correction for non-stationary regression problems. In: 2016 International Joint Conference on Neural Networks (IJCNN), IEEE, pp 771{779, DOI 10. 1109/IJCNN.2016.7727278 [OpenAIRE]

Bakirov R, Gabrys B, Fay D (2017a) Multiple adaptive mechanisms for datadriven soft sensors. Computers & Chemical Engineering 96:42{54, DOI 10.1016/ j.compchemeng.2016.08.017 [OpenAIRE]

Bakirov R, Gabrys B, Fay D (2017b) Multiple adaptive mechanisms for datadriven soft sensors. Computers and Chemical Engineering 96, DOI 10.1016/j. compchemeng.2016.08.017 [OpenAIRE]

Basak J (2006) Online adaptive decision trees: Pattern classi cation and function approximation. Neural computation 18(9):2062{2101

Bifet A, Holmes G, Gavalda R, Pfahringer B, Kirkby R (2009) New Ensemble Methods For Evolving Data Streams. Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '09 pp 139{147, DOI 10.1145/1557019.1557041

Carpenter G, Grossberg S, Reynolds J (1991) ARTMAP: Supervised real-time learning and classi cation of nonstationary data by a self-organizing neural network. Neural networks 4:565{588

Castillo G, Gama J (2006) An Adaptive Prequential Learning Framework for Bayesian Network Classi ers. In: Furnkranz J, Sche er T, Spiliopoulou M (eds) Knowledge Discovery in Databases: PKDD 2006, Springer Berlin Heidelberg, Berlin, Heidelberg, Lecture Notes in Computer Science, vol 4213, pp 67{78, DOI 10.1007/11871637

Chen Y, Keogh E, Hu B, Begum N, Bagnall A, Mueen A, Batista G (2015) The UCR Time Series Classi cation Archive

75 references, page 1 of 5
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publication . Preprint . Article . 2018

Generic adaptation strategies for automated machine learning

Bogdan Gabrys;