Testing Concept Drift Detection Technique on Data Stream
Data mutates dynamically, and these transmutations are so diverse that it affects the quality and reliability of the model. Concept Drift is the quandary of such dynamic cognitions and modifications in the data stream which leads to change in the behaviour of the model. The problem of concept drift affects the prognostication quality of the software and thus reduces its precision. In most of the drift detection methods, it is followed that there are given labels for the incipient data sample which however is not practically possible. In this paper, the performance and accuracy of the proposed concept drift detection technique for the classification of streaming data with undefined labels will be tested. Testing is followed with the creation of the centroid classification model by utilizing some training examples with defined labels and test its precision with the test set and then compare the accuracy of the prediction model with and without the proposed concept drift detection technique.
KeywordsConcept drift Data stream testing Drift detection techniques Supervised/unsupervised learning
- 1.Zliobaitė, I.: Learning under concept drift: an overview. Technical report faculty of mathematics and informatics, Vilnius University, Vilnius, Lithuania (2009)Google Scholar
- 2.Khan, L.: Data stream mining: challenges and techniques. In: Proceedings of 22nd IEEE International Conference on Tools with Artificial Intelligence (2010)Google Scholar
- 5.Wang, H., Abraham, Z.: Concept drift detection for streaming data. In: Proceedings of International Joint Conference of Neural Networks (IJCNN), Killarney, Ireland, pp. 1–9 (2015)Google Scholar
- 6.Kim, Y.I., Park, C.H.: Concept drift detection on streaming data under limited labeling. In: 2016 IEEE International Conference on Computer and Information Technology (CIT), pp. 273–280. IEEE (2016)Google Scholar
- 9.Shlens, J.: A Tutorial on Principal Component Analysis, Systems Neurobiology Laboratory, Salk Institute for Biological StudiesLa Jolla, CA 92037 and Institute for Nonlinear Science, University of California, San Diego La Jolla, CA 92093-0402, 10 December 2005. Version 2Google Scholar