Enhancing Concept Drift Classification in Computer Networks with Artificial Intelligence through NCDC-DM: A Novel Approach Utilizing Diversity Measure


  • Vaibhav B. Magdum, Rajkumar K. Chougale, Manoj Tarambale, Amrapali Shivajirao Chavan, Chetan Nimba Aher., Veena Suhas Bhende


Drift classification, online streaming, concept drift, data streaming, diversity measure


nowadays data stream mining has been essential area for research work which has been getting waste focus because it was utilized along a huge counts of applications, like telecommunication, networks of sensors & banking. Important issue was effecting mining of data stream was concept drift. When contact between target variable & input data modifications at this time. In last decade there are many classification techniques of concept drift was proposed, that either getting problem of high cost along conditions regards memory either run time either, that was not quack along conditions of classification speed. This paper proposes a technique which known as Novel Concept Drift Classification utilizing Diversity Measure (NCDC-DM), along reduction of less memory & less time it is reacting quickly.  Under proposed system there is collaboration between  disagreement measure & diversity measure, which known through static learning along scenarios of streaming utilizing test of page & utilizes these calculations comparatively along classification technique of ten drift utilizing various scenarios of drift. Outputs of research shows that proposed technique most efficient & it has capability of faster classification concept drift & it’s compared with existing ADASYN, EACD, HLFR methods.


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Albert Bifet, Ricard Gavalda, Learning through time-changing data along adap-tive windowing, along: Proceedings of the 2007 SIAM International Conference on Data Mining, SIAM, 2007, pp. 443–448.

Ali Pesaranghader, Herna L. Viktor, Fast hoeffding drift detection method for evolving data streams, along: Joint European Conference on Machine Learning & Knowledge Discovery along Databases, Springer, 2016, pp. 96–111.

Isvani Frías-Blanco, José del Campo-Ávila, Gonzalo Ramos-Jiménez, Rafael Morales-Bueno, Agustín Ortiz-Díaz, Yailé Caballero-Mota, Online & non-parametric drift detection methods based on Hoeffding’s bounds, IEEE Trans. Knowl. Data Eng. 27 (3) (2015) 810–823.

Joao Gama, Pedro Medas, Gladys Castillo, Pedro Rodrigues, Learning along drift detection, along: Brazilian Symposium on Artificial Intelligence, Springer, 2004, pp. 286–295.

Manuel Baena-Garcıa, José del Campo-Ávila, Raúl Fidalgo, Albert Bifet, R. Gavalda, R. Morales-Bueno, Early drift detection method, along: Fourth International Workshop on Knowledge Discovery through Data Streams, vol. 6, 2006, pp. 77–86.

Barros RS, Cabral DR, Gonçalves PM Jr, Santos SG (2017) RDDM: Reactive drift detection method. Expert Syst Appl 90:344–355

Mahajan, H., Reddy, K.T.V. Secure gene profile data processing using lightweight cryptography and blockchain. Cluster Comput (2023). https://doi.org/10.1007/s10586-023-04123-6.

De Lima Cabral DR, de Barros RSM (2018) Concept drift detection based on Fisher’s exact test. Inf Sci 442:220–234

E Mello RF, Vaz Y, Grossi CH, Bifet A (2019) on learning guarantees to unsupervised concept drift detection on data streams. Expert Syst Appl 117:90–102

Dua D, Graff C (2017) UCI machine learning repository. http://archive.ics.uci.edu/ml. Accessed 14 Oct 2019

Duong QH, Ramampiaro H, Nørvåg K (2018) Applying temporal dependence to detect changes along streaming data. Appl Intell 48:4805–4823

Jaworski M, Duda P, Rutkowski L (2017) New splitting criteria for decision trees along stationary data streams. IEEE Trans Neural Netw Learn Syst 29(6):2516–2529

Krawczyk B, Minku LL, Gama J, Stefanowski J, Woźniak M (2017) Ensemble learning for data stream analysis: a survey. Inf Fusion 37:132–156

Khamassi, M. Sayed-Mouchaweh, M. Hammami, & K. Ghédira, “Discussion & review on evolving data streams & concept drift adapting,” Evolving Systems 9(1), 1–23 (2018)

Z. Ahmadi & S. Kramer, “Modeling recurring concepts along data streams: a graph-based framework,” Knowledge & Information Systems 55(1), 15–44 (2018).

Khamassi, I., Sayed-Mouchaweh, M., Hammami, M. et al. Discussion & review on evolving data streams & concept drift adapting. Evolving Systems 9, 1–23 (2018).

J. Lu, A. Liu, F. Dong, F. Gu, J. Gama & G. Zhang, "Learning under Concept Drift: A Review," along IEEE Transactions on Knowledge & Data Engineering, vol. 31, no. 12, pp. 2346-2363, 1 Dec. 2019, doi: 10.1109/TKDE.2018.2876857.

Liu, A., Zhang, G., & Lu, J. (2017, July). Fuzzy time windowing for gradual concept drift adaptation. In 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (pp. 1-6). IEEE.

A. Pesaranghader, H. L. Viktor & E. Paquet, "McDiarmid Drift Detection Methods for Evolving Data Streams," 2018 International Joint Conference on Neural Networks (IJCNN), 2018, pp. 1-9, doi: 10.1109/IJCNN.2018.8489260.

Alippi, C., Qi, W., Roveri, M. (2017). Learning along Nonstationary Environments: A Hybrid Approach. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence & Soft Computing. ICAISC 2017. Lecture Notes along Computer Science(), vol 10246. Springer, Cham.

Ahmadi, Z., Kramer, S. Modeling recurring concepts along data streams: a graph-based framework. Knowl Inf Syst 55, 15–44 (2018). https://doi.org/10.1007/s10115-017-1070-0

Dhaliwal, P., & Bhatia, M. P. S. (2017). Effective handling of recurring concept drifts along data streams. Indian J. Sci. Technol, 10(30), 1-6.

Sidhu, P., Bhatia, M.P.S. A novel online ensemble approach to handle concept drifting data streams: diversified dynamic weighted majority. Int. J. Mach. Learn. & Cyber. 9, 37–61 (2018).

Y. Geng & J. Zhang, "An Ensemble Classifier Algorithm for Mining Data Streams Based on Concept Drift," 2017 10th International Symposium on Computational Intelligence & Design (ISCID), 2017, pp. 227-230, doi: 10.1109/ISCID.2017.121.

Loeffel, PX., Bifet, A., Marsala, C., Detyniecki, M. (2017). Droplet Ensemble Learning on Drifting Data Streams. In: Adams, N., Tucker, A., Weston, D. (eds) Advances along Intelligent Data Analysis XVI. IDA 2017. Lecture Notes along Computer Science(), vol 10584.

Dan Shang, Guangquan Zhang, Jie Lu, Fast concept drift detection utilizing singular vector decomposition, along: 2017 12th International Conference on Intelligent Systems & Knowledge Engineering, ISKE, 2017, pp. 1–6.

Prasad, Bakshi & Agarwal, Sonali. (2016). Stream Data Mining: Platforms, Algorithms, Performance Evaluators & Research Trends. International Journal of Database Theory & Application. 9. 201-218. 10.14257/ijdta.2016.9.9.19.

Webb, G.I., Hyde, R., Cao, H. et al. Characterizing concept drift. Data Min Knowl Disc 30, 964–994 (2016).

Haque, Ahsanul & Khan, Latifur & Baron, Michael & Thuraisingham, Bhavani & Aggarwal, Charu. (2016). Efficient handling of concept drift & concept evolution over Stream Data. 481-492. 10.1109/ICDE.2016.7498264.

Georg Krempl, Indre Žliobaite, Dariusz Brzeziński, Eyke Hüllermeier, Mark Last, Vincent Lemaire, Tino Noack, Ammar Shaker, Sonja Sievi, Myra Spiliopoulou, & Jerzy Stefanowski. 2014. Open challenges for data stream mining research. SIGKDD Explor. Newsl. 16, 1 (June 2014), 1–10. DOI:https://doi.org/10.1145/2674026.2674028.

Mahajan, H.B., Badarla, A. & Junnarkar, A.A. CL-IoT: cross-layer Internet of Things protocol for intelligent manufacturing of smart farming. J Ambient Intell Human Comput 12, 7777–7791 (2021). https://doi.org/10.1007/s12652-020-02502-0

Mahajan, H.B., & Badarla, A. (2018). Application of Internet of Things for Smart Precision Farming: Solutions and Challenges. International Journal of Advanced Science and Technology, Vol. Dec. 2018, PP. 37-45.

Mahajan, H.B., & Badarla, A. (2019). Experimental Analysis of Recent Clustering Algorithms for Wireless Sensor Network: Application of IoT based Smart Precision Farming. Jour of Adv Research in Dynamical & Control Systems, Vol. 11, No. 9. 10.5373/JARDCS/V11I9/20193162.

Mahajan, H.B., & Badarla, A. (2020). Detecting HTTP Vulnerabilities in IoT-based Precision Farming Connected with Cloud Environment using Artificial Intelligence. International Journal of Advanced Science and Technology, Vol. 29, No. 3, pp. 214 - 226.

Mikhail, A., Kamil, I. A., & Mahajan, H. (2017). Increasing SCADA System Availability by Fault Tolerance Techniques. 2017 International Conference on Computing, Communication, Control and Automation (ICCUBEA). doi:10.1109/iccubea.2017.8463911

Mikhail, A., Kareem, H. H., & Mahajan, H. (2017). Fault Tolerance to Balance for Messaging Layers in Communication Society. 2017 International Conference on Computing, Communication, Control and Automation (ICCUBEA). doi:10.1109/iccubea.2017.8463871

Alhayani, B., Abbas, S.T., Mohammed, H.J., & Mahajan, H. B. Intelligent Secured Two-Way Image Transmission Using Corvus Corone Module over WSN. Wireless Pers Commun (2021). https://doi.org/10.1007/s11277-021-08484-2.

Mahajan, H.B., Badarla, A. Cross-Layer Protocol for WSN-Assisted IoT Smart Farming Applications Using Nature Inspired Algorithm. Wireless Pers Commun 121, 3125–3149 (2021). https://doi.org/10.1007/s11277-021-08866-6

Uke, N., Pise, P., Mahajan, H.B., et.al. (2021). Healthcare 4.0 Enabled Lightweight Security Provisions for Medical Data Processing. Turkish Journal of Computer and Mathematics (2021), Vol. 12, No. 11. https://doi.org/10.17762/turcomat.v12i11.5858.

Alhayani, B., Kwekha-Rashid, A.S., Mahajan, H.B. et al. 5G standards for the Industry 4.0 enabled communication systems using artificial intelligence: perspective of smart healthcare system. Appl Nanosci (2022). https://doi.org/10.1007/s13204-021-02152-4.

Mahajan, H.B., Rashid, A.S., Junnarkar, A.A. et al. Integration of Healthcare 4.0 and blockchain into secure cloud-based electronic health records systems. Appl Nanosci (2022). https://doi.org/10.1007/s13204-021-02164-0.

Patil, S., Vaze, V., Agarkar, P. et al. Social context-aware and fuzzy preference temporal graph for personalized B2B marketing campaigns recommendations. Soft Comput (2023).

Kadam, M. V., Mahajan, H. B., Uke, N. J., & Futane, P. R. (2023). Cybersecurity threats mitigation in Internet of Vehicles communication system using reliable clustering and routing. Microprocessors and Microsystems, 102, 104926. https://doi.org/10.1016/j.micpro.2023.104926.




How to Cite

Chetan Nimba Aher., Veena Suhas Bhende, V. B. M. R. K. C. M. T. A. S. C. . (2024). Enhancing Concept Drift Classification in Computer Networks with Artificial Intelligence through NCDC-DM: A Novel Approach Utilizing Diversity Measure. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 1196–1205. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5572



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