A Novel Drift Detection Algorithm Based on Features’ Importance Analysis in a Data Streams Environment

Piotr Duda 1 , Krzysztof Przybyszewski 2  and Lipo Wang 3
  • 1 Department of Computer Engineering, Czestochowa University of Technology, Częstochowa, Poland
  • 2 Information Technology Institute, University of Social Sciences, Clark University, , 90-113, Łódz
  • 3 Nanyang Technological University, School of Electrical and Electronic Engineering, Singapore

Abstract

The training set consists of many features that influence the classifier in different degrees. Choosing the most important features and rejecting those that do not carry relevant information is of great importance to the operating of the learned model. In the case of data streams, the importance of the features may additionally change over time. Such changes affect the performance of the classifier but can also be an important indicator of occurring concept-drift. In this work, we propose a new algorithm for data streams classification, called Random Forest with Features Importance (RFFI), which uses the measure of features importance as a drift detector. The RFFT algorithm implements solutions inspired by the Random Forest algorithm to the data stream scenarios. The proposed algorithm combines the ability of ensemble methods for handling slow changes in a data stream with a new method for detecting concept drift occurrence. The work contains an experimental analysis of the proposed algorithm, carried out on synthetic and real data.

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  • [1] P. Duda, M. Jaworski, L. Pietruczuk, and L. Rutkowski, A novel application of Hoeffding’s inequality to decision trees construction for data streams, in Neural Networks (IJCNN), 2014 International Joint Conference on. IEEE, 2014, pp. 3324–3330.

  • [2] L. Rutkowski, L. Pietruczuk, P. Duda, and M. Jaworski, Decision trees for mining data streams based on the McDiarmid’s bound, IEEE Transactions on Knowledge and Data Engineering, vol. 25, no. 6, pp. 1272–1279, 2013.

  • [3] L. Rutkowski, M. Jaworski, L. Pietruczuk, and P. Duda, Decision trees for mining data streams based on the Gaussian approximation, IEEE Transactions on Knowledge and Data Engineering, vol. 26, no. 1, pp. 108–119, 2014.

  • [4] L. Rutkowski, M. Jaworski, L. Pietruczuk, and P. Duda, The CART decision tree for mining data streams, Information Sciences, vol. 266, pp. 1–15, 2014.

  • [5] L. Pietruczuk, L. Rutkowski, M. Jaworski, and P. Duda, The parzen kernel approach to learning in non-stationary environment, in Neural Networks (IJCNN), 2014 International Joint Conference on. IEEE, 2014, pp. 3319–3323.

  • [6] L. Rutkowski, M. Jaworski, L. Pietruczuk, and P. Duda, A new method for data stream mining based on the misclassification error, IEEE Transactions on Neural Networks and Learning Systems, vol. 26, no. 5, pp. 1048–1059, 2015.

  • [7] P. Duda, M. Jaworski, and L. Rutkowski, Knowledge discovery in data streams with the orthogonal series-based generalized regression neural networks, Information Sciences,, 2017.

  • [8] M. Jaworski, P. Duda, and L. Rutkowski, New splitting criteria for decision trees in stationary data streams, IEEE Transactions on Neural Networks and Learning Systems, vol. PP, no. 99, pp. 1–14, 2017.

  • [9] M. Jaworski, P. Duda, L. Rutkowski, P. Najgebauer, and M. Pawlak, Heuristic regression function estimation methods for data streams with concept drift, in Lecture Notes in Computer Science. Springer, 2017, pp. 726–737.

  • [10] M. Jaworski, P. Duda, and L. Rutkowski, On applying the restricted boltzmann machine to active concept drift detection, in Computational Intelligence (SSCI), 2017 IEEE Symposium Series on. IEEE, 2017, pp. 1–8.

  • [11] M. Jaworski, Regression function and noise variance tracking methods for data streams with concept drift, International Journal of Applied Mathematics and Computer Science, vol. 28, no. 3, pp. 559–567, 2018.

  • [12] P. Duda, M. Jaworski, and L. Rutkowski, Convergent time-varying regression models for data streams: Tracking concept drift by the recursive parzen-based generalized regression neural networks, International Journal of Neural Systems, vol. 28, no. 02, p. 1750048, 2018.

  • [13] P. Duda, M. Jaworski, A. Cader, and L. Wang, On training deep neural networks using a streaming approach, Journal of Artificial Intelligence and Soft Computing Research, vol. 10, no. 1, 2020.

  • [14] A. Lall, V. Sekar, M. Ogihara, J. Xu, and H. Zhang, Data streaming algorithms for estimating entropy of network traffic, in ACM SIGMETRICS Performance Evaluation Review, vol. 34, no. 1. ACM, 2006, pp. 145–156.

  • [15] C. Phua, V. Lee, K. Smith, and R. Gayler, A comprehensive survey of data mining-based fraud detection research, arXiv preprint arXiv:1009.6119, 2010.

  • [16] A. Dal Pozzolo, G. Boracchi, O. Caelen, C. Alippi, and G. Bontempi, Credit card fraud detection: A realistic modeling and a novel learning strategy, IEEE transactions on neural networks and learning systems, vol. 29, no. 8, p. 3784–3797, August 2018.

  • [17] S. Disabato and M. Roveri, Learning convolutional neural networks in presence of concept drift, in 2019 International Joint Conference on Neural Networks (IJCNN), 2019, pp. 1–8.

  • [18] W. N. Street and Y. Kim, A streaming ensemble algorithm (sea) for large-scale classification, in Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2001, pp. 377–382.

  • [19] N. C. Oza, Online bagging and boosting, in Systems, man and cybernetics, 2005 IEEE international conference on, vol. 3. IEEE, 2005, pp. 2340–2345.

  • [20] P. Duda, On ensemble components selection in data streams scenario with gradual concept-drift, in International Conference on Artificial Intelligence and Soft Computing. Springer, 2018, pp. 311–320.

  • [21] P. Duda, M. Jaworski, and L. Rutkowski, On ensemble components selection in data streams scenario with reoccurring concept-drift, in 2017 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2017, pp. 1–7.

  • [22] L. Pietruczuk, L. Rutkowski, M. Jaworski, and P. Duda, A method for automatic adjustment of ensemble size in stream data mining, in Neural Networks (IJCNN), 2016 International Joint Conference on. IEEE, 2016, pp. 9–15.

  • [23] L. Pietruczuk, L. Rutkowski, M. Jaworski, and P. Duda, How to adjust an ensemble size in stream data mining? Information Sciences, vol. 381, pp. 46–54, 2017.

  • [24] G. Ditzler, M. Roveri, C. Alippi, and R. Polikar, Learning in nonstationary environments: A survey, IEEE Computational Intelligence Magazine, vol. 10, no. 4, pp. 12–25, 2015.

  • [25] P. Duda, L. Rutkowski, M. Jaworski, and D. Rutkowska, On the Parzen kernel-based probability density function learning procedures over time-varying streaming data with applications to pattern classification, IEEE transactions on cybernetics, vol 50, no. 4, pp. 1683-1696, 2020.

  • [26] E. Rafajlowicz, W. Rafajlowicz, Testing (non-) linearity of distributed-parameter systems from a video sequence, Asian Journal of Control, Vol. 12, no. 2, pp. 146–158, 2010.

  • [27] E. Rafajlowicz, H. Pawlak-Kruczek, W. Rafajlowicz, Statistical Classifier with Ordered Decisions as an Image Based Controller with Application to Gas Burners, Springer, Lecture Notes in Artificial Intelligence, vol. 8467, pp. 586–597, 2014.

  • [28] E. Rafajlowicz, W. Rafajlowicz, Iterative learning in optimal control of linear dynamic processes, International Journal Of Control, vol. 91, no. 7, pp. 1522–1540, 2018.

  • [29] P. Jurewicz, W. Rafajlowicz, J. Reiner, et al., Simulations for Tuning a Laser Power Control System of the Cladding Process, Lecture Notes in Computer Science, vol. 9842, pp. 218–229, Springer, 2016.

  • [30] E. Rafajlowicz, W. Rafajlowicz, Iterative Learning in Repetitive Optimal Control of Linear Dynamic Processes, 15th International Conference on Artificial Intelligence and Soft Computing (ICAISC), 2016, Springer, vol. 9692, pp. 705–717, 2016.

  • [31] E. Rafajlowicz, W. Rafajlowicz, Control of linear extended nD systems with minimized sensitivity to parameter uncertainties, Multidimensional Systems And Signal Processing, vol. 24, no. 4, pp. 637–656, 2013.

  • [32] S. A. Ludwig, Applying a neural network ensemble to intrusion detection, Journal of Artificial Intelligence and Soft Computing Research, vol. 9, no. 3, pp. 177–188, 2019.

  • [33] H. Wang, W. Fan, P. S. Yu, and J. Han, Mining concept-drifting data streams using ensemble classifiers, in Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining. AcM, 2003, pp. 226–235.

  • [34] R. Polikar, L. Upda, S. S. Upda, and V. Honavar, Learn++: An incremental learning algorithm for supervised neural networks, IEEE transactions on systems, man, and cybernetics, part C (applications and reviews), vol. 31, no. 4, pp. 497–508, 2001.

  • [35] R. Elwell and R. Polikar, Incremental learning of concept drift in nonstationary environments, IEEE Transactions on Neural Networks, vol. 22, no. 10, pp. 1517–1531, 2011.

  • [36] A. Beygelzimer, S. Kale, and H. Luo, Optimal and adaptive algorithms for online boosting, in Proceedings of the 32nd International Conference on Machine Learning (ICML-15), 2015, pp. 2323–2331.

  • [37] H. M. Gomes, J. P. Barddal, F. Enembreck, and A. Bifet, A survey on ensemble learning for data stream classification, ACM Computing Surveys (CSUR), vol. 50, no. 2, p. 23, 2017.

  • [38] B. Krawczyk, L. L. Minku, J. Gama, J. Stefanowski, and M. Wozniak, Ensemble learning for data stream analysis: A survey, Information Fusion, vol. 37, pp. 132–156, 2017.

  • [39] L. Breiman, Random forests, Machine learning, vol. 45, no. 1, pp. 5–32, 2001.

  • [40] H. Abdulsalam, D. B. Skillicorn, and P. Martin, Classifying evolving data streams using dynamic streaming random forests, in International Conference on Database and Expert Systems Applications. Springer, 2008, pp. 643–651.

  • [41] H. Abdulsalam, P. Martin, and D. Skillicorn, Streaming random forests, 2008.

  • [42] H. M. Gomes, A. Bifet, J. Read, J. P. Barddal, F. Enembreck, B. Pfharinger, G. Holmes, and T. Abdessalem, Adaptive random forests for evolving data stream classification, Machine Learning, vol. 106, no. 9-10, pp. 1469–1495, 2017.

  • [43] P. Domingos and G. Hulten, Mining high-speed data streams, in Proc. 6th ACM SIGKDD Internat. Conf. on Knowledge Discovery and Data Mining, 2000, pp. 71–80.

  • [44] A. Bifet and R. Gavaldà, Adaptive learning from evolving data streams, in International Symposium on Intelligent Data Analysis. Springer, 2009, pp. 249–260.

  • [45] E. S. Page, Continuous inspection schemes, Biometrika, vol. 41, no. 1/2, pp. 100–115, 1954.

  • [46] J. P. Barddal, H. M. Gomes, F. Enembreck, and B. Pfahringer, A survey on feature drift adaptation: Definition, benchmark, challenges and future directions, Journal of Systems and Software, 07 2016.

  • [47] H.-L. Nguyen, Y.-K. Woon, W.-K. Ng, and L. Wan, Heterogeneous ensemble for feature drifts in data streams, in Pacific-Asia Conference on Knowledge Discovery and Data Mining. Springer, 2012, pp. 1–12.

  • [48] A. P. Cassidy and F. A. Deviney, Calculating feature importance in data streams with concept drift using online random forest, in 2014 IEEE International Conference on Big Data (Big Data). IEEE, 2014, pp. 23–28.

  • [49] R. Zhu, D. Zeng, and M. R. Kosorok, Reinforcement learning trees, Journal of the American Statistical Association, vol. 110, no. 512, pp. 1770–1784, 2015.

  • [50] L. Yuan, B. Pfahringer, and J. P. Barddal, Iterative subset selection for feature drifting data streams, in Proceedings of the 33rd Annual ACM Symposium on Applied Computing. ACM, 2018, pp. 510–517.

  • [51] L. C. Molina, L. Belanche, and À. Nebot, Feature selection algorithms: A survey and experimental evaluation, in 2002 IEEE International Conference on Data Mining, 2002. Proceedings. IEEE, 2002, pp. 306–313.

  • [52] G. Ditzler, J. LaBarck, J. Ritchie, G. Rosen, and R. Polikar, Extensions to online feature selection using bagging and boosting, IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 9, pp. 4504–4509, 2018.

  • [53] J. P. Barddal, H. M. Gomes, F. Enembreck, and B. Pfahringer, A survey on feature drift adaptation: Definition, benchmark, challenges and future directions, Journal of Systems and Software, 07 2016.

  • [54] J. Gama, P. Medas, G. Castillo, and P. Rodrigues, Learning with drift detection, in Brazilian symposium on artificial intelligence. Springer, 2004, pp. 286–295.

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