# Concept Drift Adaptation with Incremental-Decremental SVM
## 4 Adaptive-Window Incremental-Decremental SVM (AIDSVM)
[CrossRef]
* (16) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Modeling Pan Evaporation Using Gaussian Process Regression K-Nearest Neighbors Random Forest and Support Vector Machines. Comparative Analysis. _Atmosphere_**2020**, _11_, 66. [CrossRef]
* (17) [PERSON]; [PERSON] Detecting concept drift with support vector machines. In Proceedings of the Seventeenth International Conference on Machine Learning (ICML '00), Stanford, CA, USA, 29 June-2 July 2000; ICML: San Diego, CA, USA, 2000; pp. 487-494. * (18) [PERSON] Learning drifting concepts: Example selection vs. example weighting. _Intell. Data Anal._**2004**, \(8\), 281-300. [CrossRef]
* (19) [PERSON]; [PERSON]; [PERSON] Concept Drift-Oriented Adaptive and Dynamic Support Vector Machine Ensemble With Time Window in Corporate Financial Risk Prediction. _IEEE Trans. Syst. Man Cybern. Syst._**2013**, _43_, 801-813. [CrossRef]
* (20) [PERSON]; [PERSON] Learning from time-changing data with adaptive windowing. In Proceedings of the Seventh SIAM International Conference on Data Mining (SDM'07), Minneapolis, MN, USA, 26-28 April 2007; Volume 7, p. 6. * (21) [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] An overview of unsupervised drift detection methods. _WIREs Data Min. Knowl. Discov._**2020**, \(6\), e1381. [CrossRef]
* (22) [PERSON]; [PERSON] Incremental learning in nonstationary environments with controlled forgetting. In Proceedings of the 2009 International Joint Conference on Neural Networks, Atlanta, Ga, USA, 14-19 June 2009; pp. 771-778. * (23) [PERSON]; [PERSON] Incremental learning of concept drift in nonstationary environments. _IEEE Trans. Neural Netw._**2011**, _22_, 1517-1531. [CrossRef] [PubMed]
* (24) [PERSON]; [PERSON]; [PERSON]; [PERSON] A Fast Learn++.NSE classification algorithm based on weighted moving average. _Filomat_**2018**, _32_, 1737-1745. [CrossRef]
* (25) [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Adaptive random forests for evolving data stream classification. _Mach. Learn._**2017**, _106_, 1469-1495. [CrossRef]
* (26) [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON] Learning with drift detection. In Proceedings of the 17 th Brazilian Symposium on Artificial Intelligence (SBIA 2004), Sao Luis, Maranhao, Brazil, 29 September-1 October 2004. * (27) [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Early drift detection method. In Proceedings of the Fourth International Workshop on Knowledge Discovery from Data Streams, San Francisco, CA, USA, 2006. Volume 6, pp. 77-86. * (28) [PERSON]; [PERSON] Fast Hoeffding drift detection method for evolving data streams. In Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Riva del Garda, Italy, 19-23 September 2016; pp. 96-111. * [PERSON] and [PERSON] (2016) [PERSON]; [PERSON]; [PERSON] [PERSON] A framework for classification in data streams using multi-strategy learning. In _International Conference on Discovery Science_; Springer: Cham, Switzerland, 2016; pp. 341-355.22. [CrossRef]
* [PERSON] and [PERSON] (2018) [PERSON]; [PERSON]; [PERSON] [PERSON] Reservoir of diverse adaptive learners and stacking fast Hoeffding drift detection methods for evolving data streams. _Mach. Learn. J._**2018**, _107_, 1711-1743. [CrossRef]
* [PERSON] (2018) [PERSON] A Reservoir of Adaptive Algorithms for Online Learning from Evolving Data Streams. Ph.D. Dissertation, University of Ottawa, Ottawa, ON, Canada, 2018. [CrossRef]
* [PERSON] (2020) [PERSON]. Accurate detecting concept drift in evolving data streams. _ICT Express_**2020**, \(6\), 332-338. [CrossRef]
* [PERSON] et al. (2016) [PERSON]; [PERSON]; [PERSON] [PERSON] A support vector based approach for classification beyond the learned label space in data streams. In Proceedings of the 31 st Annual ACM Symposium on Applied Computing (SAC '16), Pisa Italy, 4-8 April 2016; Association for Computing Machinery: New York, NY, USA, 2016; pp. 910-915. * [PERSON] et al.