[
[1] A. Al-Fatlawi, H. Fatlawi, S. H. Ling Recognition physical activities with optimal number of wearable sensors using data mining algorithms and deep belief network, 2017 39th annual international conference of the IEEE engineering in medicine and biology society (EMBC) Seogwipo, South Korea, 2017, pp. 2871–2874. ⇒710.1109/EMBC.2017.803745629060497
]Search in Google Scholar
[
[2] B. Babenko, MH. Yang, S. Belongie, A family of online boosting algorithms, 2009 IEEE 12th international conference on computer vision workshops, ICCV workshops Kyoto, Japan, 2009, pp. 1346–1353. ⇒6, 7, 810.1109/ICCVW.2009.5457453
]Search in Google Scholar
[
[3] P. R. Bhaladhare, D. C. Jinwala, Novel Approaches for Privacy Preserving Data Mining in k-Anonymity Model, Journal of information science and engineering, 32 (2016) 63–78. ⇒3, 5
]Search in Google Scholar
[
[4] A. Bifet, G. Holmes, B. Pfahringer, G. Bernhard, Improving adaptive bagging methods for evolving data streams, Asian conference on machine learning Nanjing, China, 2009, pp. 23–37. ⇒1110.1007/978-3-642-05224-8_4
]Search in Google Scholar
[
[5] K. Chaudhuri, C. Monteleoni, Privacy-preserving logistic regression, Advances in neural information processing systems Vancouver, Canada, 2009, pp. 289–296. ⇒3, 5, 8
]Search in Google Scholar
[
[6] P. Domingos, G. Hulten, Mining high-speed data streams, KDD00: the second annual international conference on knowledge discovery in data Boston Massachusetts, USA, 2000, pp. 71–80. ⇒1010.1145/347090.347107
]Search in Google Scholar
[
[7] C. Dwork, G. N. Rothblum, S. Vadhan, Boosting and differential privacy, 2010 IEEE 51st annual symposium on foundations of computer science Las Vegas, Nevada USA, 2010, pp. 51–60. ⇒3, 5, 810.1109/FOCS.2010.12
]Search in Google Scholar
[
[8] H. Fatlawi, A. Kiss, On Robustness of Adaptive Random Forest Classifier on Biomedical Data Stream, Asian Conference on Intelligent Information and Database Systems (ACIIDS 2020), Lecture notes in computer science Springer, 12033 (2020) 332-344. ⇒410.1007/978-3-030-41964-6_29
]Search in Google Scholar
[
[9] J. Gama, Knowledge discovery from data streams, The CRC Press, 2010. ⇒6, 710.1201/EBK1439826119
]Search in Google Scholar
[
[10] G. Giovanni, J. F. Elder, Ensemble methods in data mining: improving accuracy through combining predictions, The Synthesis lectures on data mining and knowledge discovery Morgan & Claypool Publishers, 2010. ⇒7
]Search in Google Scholar
[
[11] G. Hulten, L. Spencer, P. Domingos, Mining time-changing data streams, Proceedings of the seventh ACM SIGKDD international conference on knowledge discovery and data mining San Francisco California, USA, 2001, pp. 97–106. ⇒210.1145/502512.502529
]Search in Google Scholar
[
[12] M. A. Kadampur, D.V.L.N Somayajulu, A noise addition scheme in decision tree for privacy preserving data mining, Journal of computing 2, 1 (2010) 137–144. ⇒3, 5
]Search in Google Scholar
[
[13] M. Kuhn, K. Johnson, Applied predictive modeling, Springer, 2013. ⇒710.1007/978-1-4614-6849-3
]Search in Google Scholar
[
[14] L. Li, R. Lu, K. R. Choo, A. Datta,J. Shao, Privacy-preserving-outsourced association rule mining on vertically partitioned databases, IEEE transactions on information forensics and security, 11, 8 (2016) 1847–1861. ⇒3, 510.1109/TIFS.2016.2561241
]Search in Google Scholar
[
[15] V. Manikandan, V. Porkodi, A. S. Mohammed, M. Sivaram, Privacy preserving data Mining using threshold based fuzzy C-Means clustering, ICTACT journal on soft computing, 9, 1 (2018) 1820–1823. ⇒4, 5
]Search in Google Scholar
[
[16] J. J. v. Nayahi, V. Kavitha, Privacy and utility preserving data clustering for data anonymization and distribution on Hadoop, Future Generation Computer Systems, 74 (2017) 393–408. ⇒4, 510.1016/j.future.2016.10.022
]Search in Google Scholar
[
[17] T. Ngo, Data mining: practical machine learning tools and technique, by ian h. witten, eibe frank, mark a. hell, The ACM SIGSOFT Software Engineering Notes, 36, 5 2011. ⇒710.1145/2020976.2021004
]Search in Google Scholar
[
[18] N.C. Oza, Online bagging and boosting, 2005 IEEE international conference on systems, man and cybernetics Waikoloa, HI, USA, 2005, pp. 2340–2345. ⇒10
]Search in Google Scholar
[
[19] D. L. Quoc, M. Beck, P. Bhatotia, R. Chen, Christof Fetzer, Thorsten Strufe, PrivApprox: privacy-preserving stream analytics, 2017 annual technical conference (USENIX ATC ’17) Santa Clara, CA, USA 2017, pp. 659–672. ⇒4, 5
]Search in Google Scholar
[
[20] J. Vaidya, B. Shafiq, W. Fan, D. Mehmood, D. Lorenzi, A random decision tree framework for privacy-preserving data mining, IEEE transactions on dependable and secure computing, 11, 5 (2013) 399–411. ⇒3, 510.1109/TDSC.2013.43
]Search in Google Scholar
[
[21] Y. Wang, X. Wu, D. Hu, Using Randomized Response for Differential Privacy Preserving Data Collection, EDBT/ICDT Workshops, Bordeaux, France, 1558 (2016). ⇒3, 5
]Search in Google Scholar
[
[22] L. Zhang, Y. Liu, R. Wang, X. Fu, Q. Lin, E cient privacy-preserving classification construction model with differential privacy technology, Journal of systems engineering and electronics BIAI, 28, 1 (2017) 170–178. ⇒4, 510.21629/JSEE.2017.01.19
]Search in Google Scholar
[
[23] Z. Zhou, Ensemble methods: foundations and algorithms, The CRC press, 2012. ⇒710.1201/b12207
]Search in Google Scholar
[
[24] T. Zhu, G. Li, W. Zhou, S. Y. Philip, Differential privacy and applications, Springer, 2017. ⇒8, 10
]Search in Google Scholar
