Abstract
The change in data distribution over time (known as concept drift) makes the classification process complex because of the discrepancy between current and incoming data distribution. A plethora of drift detection methods often focus on the early identification of concept drift. Along with the drift, other deformities like noise and blips are also present in the data stream. These deformities may be damaged the underlying learning system by forcing adaptation to false drift. Thereby unnecessary update performs in the learning model that leads to decrease in learner’s accuracy. The existing drift detection methods are not capable of differentiating between actual and false drift. The paper proposes DBDDM, a disposition-based drift detection method, to overcome the issue of false drift. In this paper, we utilize the approximate randomization test to find the frequency of consecutive drift and compare the obtained frequency with the threshold to determine the actual drift. DBDDM compares with the several state-of-the-art methods using synthetic and real-time datasets. It exhibits a maximum increase in accuracy of 24% and 28% with a rise of 2.50 and 1.91 average ranks using Naive Bayes and the Hoeffding tree classifier, respectively.
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References
Gama, J.; Žliobaite, I.: A survey on concept drift adaptation. In: 2015 International Joint Conference on Neural Networks (IJCNN). ACM Computing Survey, pp. 1–37 (2014)
Lu, J.; Liu, A.; Song, Y.; Zhang, G.: Data-driven decision support under concept drift in streamed big data. Complex Intell. Syst. 6(1), 157–163 (2020)
de Barros, R.S.M.; Hidalgo, J.I.G.; de Lima Cabral, D.R.: Wilcoxon rank sum test drift detector. Neurocomputing 275, 1954–1963 (2018)
Frías-Blanco, I.; del Campo-Ávila, J.; Ramos-Jimenez, G.; Morales-Bueno, R.; Ortiz-Díaz, A.; Caballero-Mota, Y.: Online and non-parametric drift detection methods based on Hoeffding’s bounds. IEEE Trans. Knowl. Data Eng. 27(3), 810–823 (2014)
Shao, J.; Ahmadi, Z.; Kramer, S.: Prototype-based learning on concept-drifting data streams. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 412–421 (2014)
Lu, N.; Lu, J.; Zhang, G.; De Mantaras, R.L.: A concept drift-tolerant case-base editing technique. Artif. Intell. 230, 108–133 (2016)
Liu, A.; Lu, J.; Liu, F.; Zhang, G.: Accumulating regional density dissimilarity for concept drift detection in data streams. Pattern Recognit. 76, 256–272 (2018)
Korycki, Ł.; Krawczyk, B. Adversarial concept drift detection under poisoning attacks for robust data stream mining (2020). arXiv preprint arXiv:200909497
Hulten, G.; Spencer, L.; Domingos, P.: Mining time-changing data streams. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, pp. 97–106 (2001)
Masud, M.M.; Gao, J.; Khan, L.; Han, J.; Thuraisingham, B.: A multi-partition multi-chunk ensemble technique to classify concept-drifting data streams. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer, pp. 363–375 (2009)
Abdulsalam, H.; Skillicorn, D.B.; Martin, P.: Classification using streaming random forests. IEEE Trans. Knowl. Data Eng. 23(1), 22–36 (2010)
Yu, S.; Abraham, Z.; Wang, H.; Shah, M.; Wei, Y.; Príncipe, J.C.: Concept drift detection and adaptation with hierarchical hypothesis testing. J. Frank. Inst. 356(5), 3187–3215 (2019)
Brzezinski, D.; Stefanowski, J.: Reacting to different types of concept drift: the accuracy updated ensemble algorithm. IEEE Trans. Neural Netw. Learn. Syst. 25(1), 81–94 (2013)
Pesaranghader, A.; Viktor, H.L.: Fast Hoeffding drift detection method for evolving data streams. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Springer, pp. 96–111 (2016)
Agrahari, S.; Singh, A.K.: Concept drift detection in data stream mining: a literature review. J. King Saud Univ. Comput. Inf. Sci. (2021). https://doi.org/10.1016/j.jksuci.2021.11.006
Gama, J.; Medas, P.; Castillo, G.; Rodrigues, P.: Learning with drift detection. In: Brazilian Symposium on Artificial Intelligence, Springer, pp. 286–295 (2004)
Gama, J.; Castillo, G.: Learning with local drift detection. In: International Conference on Advanced Data Mining and Applications, Springer, pp. 42–55 (2006)
Nishida, K.: Learning and Detecting Concept Drift. Information Science and Technology (2008)
Liu, A.; Song, Y.; Zhang, G.; Lu, J.: Regional concept drift detection and density synchronized drift adaptation. In: IJCAI International Joint Conference on Artificial Intelligence (2017)
Liu, A.; Zhang, G.; Lu, J.: Fuzzy time windowing for gradual concept drift adaptation. In: 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE, pp. 1–6 (2017)
Baena-Garcıa, M.; del Campo-Ávila, J.; Fidalgo, R.; Bifet, A.; Gavalda, R.; Morales-Bueno, R.: Early drift detection method. Fourth Int. Workshop Knowl. Discov. Data Streams 6, 77–86 (2006)
Ross, G.J.; Adams, N.M.; Tasoulis, D.K.; Hand, D.J.: Exponentially weighted moving average charts for detecting concept drift. Pattern Recognit. Lett. 33(2), 191–198 (2012)
Bifet, A.: Adaptive learning and mining for data streams and frequent patterns. ACM SIGKDD Explor. Newsl. 11(1), 55–56 (2009)
Bifet, A.; Gavalda, R.: Learning from time-changing data with adaptive windowing. In: Proceedings of the 2007 SIAM International Conference on Data Mining, SIAM, pp. 443–448 (2007)
Huang, D.T.J.; Koh, Y.S.; Dobbie, G.; Pears, R.: Detecting volatility shift in data streams. In: 2014 IEEE International Conference on Data Mining, pp. 863–868. https://doi.org/10.1109/ICDM.2014.50 (2014)
Pears, R.; Sakthithasan, S.; Koh, Y.S.: Detecting concept change in dynamic data streams. Mach. Learn. 97(3), 259–293 (2014)
Raza, H.; Prasad, G.; Li, Y.: EWMA model based shift-detection methods for detecting covariate shifts in non-stationary environments. Pattern Recognit. 48(3), 659–669 (2015)
Alippi, C.; Boracchi, G.; Roveri, M.: Hierarchical change-detection tests. IEEE Trans. Neural Netw. Learn. Syst. 28(2), 246–258 (2016)
Yu, S.; Abraham, Z.: Concept drift detection with hierarchical hypothesis testing. In: Proceedings of the 2017 SIAM International Conference on Data Mining, SIAM, pp. 768–776 (2017)
Miyata, Y.; Ishikawa, H.: Concept drift detection on data stream for revising DBSCAN cluster. In: Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics, pp. 104–110 (2020)
Gu, F.; Zhang, G.; Lu, J.; Lin, C.T.: Concept drift detection based on equal density estimation. In: 2016 International Joint Conference on Neural Networks (IJCNN), IEEE, pp. 24–30 (2016)
Song, X.; Wu, M.; Jermaine, C.; Ranka, S.: Statistical change detection for multi-dimensional data. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 667–676 (2007)
Bu, L.; Alippi, C.; Zhao, D.: A pdf-free change detection test based on density difference estimation. IEEE Trans. Neural Netw. Learn. Syst. 29(2), 324–334 (2016)
Bu, L.; Zhao, D.; Alippi, C.: An incremental change detection test based on density difference estimation. IEEE Trans. Syst. Man Cybern. Syst. 47(10), 2714–2726 (2017)
Nishida, K.; Yamauchi, K.: Detecting concept drift using statistical testing. In: Corruble, V., Takeda, M., Suzuki, E. (eds.) Discovery Science, pp. 264–269. Springer, Berlin (2007)
Mahdi, O.A.; Pardede, E.; Ali, N.: A hybrid block-based ensemble framework for the multi-class problem to react to different types of drifts. Cluster Comput. 24(3), 2327–2340 (2021)
Mahdi, O.A.; Pardede, E.; Ali, N.: kappa as drift detector in data stream mining. Procedia Comput. Sci. 184, 314–321 (2021)
Mehmood, H.; Kostakos, P.; Cortes, M.; Anagnostopoulos, T.; Pirttikangas, S.; Gilman, E.: Concept drift adaptation techniques in distributed environment for real-world data streams. Smart Cities 4(1), 349–371 (2021)
Heusinger, M.; Schleif, F.M.: reactive concept drift detection using coresets over sliding windows. In: 2020 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, pp. 1350–1355 (2020)
Misra, S.; Biswas, D.; Saha, S.K.; Mazumdar, C.: Applying Fourier inspired windows for concept drift detection in data stream. In: 2020 IEEE Calcutta Conference (CALCON), IEEE, pp. 152–156 (2020)
Mahdi, O.A.; Pardede, E.; Ali, N.; Cao, J.: Diversity measure as a new drift detection method in data streaming. Knowl. Based Syst. 191, 105227 (2020)
Sakthithasan, S.; Pears, R.; Koh, Y.S.: One pass concept change detection for data streams. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds.) Advances in Knowledge Discovery and Data Mining, pp. 461–472. Springer, Berlin (2013)
Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)
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Agrahari, S., Singh, A.K. Disposition-Based Concept Drift Detection and Adaptation in Data Stream. Arab J Sci Eng 47, 10605–10621 (2022). https://doi.org/10.1007/s13369-022-06653-4
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DOI: https://doi.org/10.1007/s13369-022-06653-4