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  2. Vol 7, No 3 (2023)
  3. Lebedev

Adaptive Learning and Integrated Use of Information Flow Forecasting Methods

Ilya S. Lebedev, Mikhail E. Sukhoparov

Abstract


This research aims to improve quality indicators in solving classification and regression problems based on the adaptive selection of various machine learning models on separate data samples from local segments. The proposed method combines different models and machine learning algorithms on individual subsamples in regression and classification problems based on calculating qualitative indicators and selecting the best models on local sample segments. Detecting data changes and time sequences makes it possible to form samples where the data have different properties (for example, variance, sample fraction, data span, and others). Data segmentation is used to search for trend changes in an algorithm for points in a time series and to provide analytical information. The experiment performance used actual data samples and, as a result, obtained experimental values of the loss function for various classifiers on individual segments and the entire sample. In terms of practical novelty, it is possible to use the obtained results to increase quality indicators in classification and regression problem solutions while developing models and machine learning methods. The proposed method makes it possible to increase classification quality indicators (F-measure, Accuracy, AUC) and forecasting (RMSE) by 1%–8% on average due to segmentation and the assignment of models with the best performance in individual segments.

 

Doi: 10.28991/ESJ-2023-07-03-03

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Keywords


Machine Learning; Segmentation; Adaptive Learning; Information Local Properties.

References


Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., & Zhang, G. (2019). Learning under Concept Drift: A Review. IEEE Transactions on Knowledge and Data Engineering, 31(12), 2346–2363. doi:10.1109/TKDE.2018.2876857.

Marinin, M., Karasev, M., Pospehov, G., Pomortseva, A., Kondakova, V., & Sushkova, V. (2023). Comprehensive study of filtration properties of pelletized sandy clay ores and filtration modes in the heap leaching stack. Journal of Mining Institute, 259, 30–40. doi:10.31897/pmi.2023.7.

Blyth, C. R. (1972). On Simpson’s Paradox and the Sure-Thing Principle. Journal of the American Statistical Association, 67(338), 364. doi:10.2307/2284382.

Tsai, S.-Y., & Chang, J.-Y. (2018). Parametric study and design of deep learning on leveling system for smart manufacturing. 2018 IEEE International Conference on Smart Manufacturing, Industrial & Logistics Engineering (SMILE). doi:10.1109/smile.2018.8353980.

Dang, Q., & Yuan, J. (2023). A Kalman filter-based prediction strategy for multiobjective multitasking optimization. Expert Systems with Applications, 213(B), 119025. doi:10.1016/j.eswa.2022.119025.

Pouyanfar, S., Sadiq, S., Yan, Y., Tian, H., Tao, Y., Reyes, M. P., Shyu, M. L., Chen, S. C., & Iyengar, S. S. (2018). A survey on deep learning: Algorithms, techniques, and applications. ACM Computing Surveys, 51(5), 1–36. doi:10.1145/3234150.

Pedersen, T. (2000). A simple approach to building ensembles of naive Bayesian classifiers for word sense disambiguation. arXiv Preprint cs/0005006. doi:10.48550/arXiv.cs/0005006

Sethi, T. S., & Kantardzic, M. (2018). Handling adversarial concept drift in streaming data. Expert Systems with Applications, 97, 18–40. doi:10.1016/j.eswa.2017.12.022.

Cerqueira, V., Torgo, L., & Soares, C. (2019). Machine Learning vs Statistical Methods for Time Series Forecasting: Size Matters. ArXiv. doi:10.48550/arXiv.1909.13316.

Liu, C., Fu, L., Li, H., & Chen, B. (2023). Dynamic Prediction Algorithm for Low-Voltage Distribution Network Power Loss in a Smart City Based on Classification Decision Tree and Marketing Data. Journal of Testing and Evaluation, 51(3), 20220096. doi:10.1520/JTE20220096.

Ye, X., & Zhao, J. (2023). Heterogeneous clustering via adversarial deep Bayesian generative model. Frontiers of Computer Science, 17(3), 173322. doi:10.1007/s11704-022-1376-2.

Zhou, Z. H., & Feng, J. (2019). Deep forest. National Science Review, 6(1), 74–86. doi:10.1093/nsr/nwy108.

Muksin, U., Riana, E., Rudyanto, A., Bauer, K., Simanjuntak, A. V. H., & Weber, M. (2023). Neural network-based classification of rock properties and seismic vulnerability. Global Journal of Environmental Science and Management, 9(1), 15–30. doi:10.22034/gjesm.2023.01.02.

Kim, J. Y., Kim, D., Li, Z. J., Dariva, C., Cao, Y., & Ellis, N. (2023). Predicting and optimizing syngas production from fluidized bed biomass gasifiers: A machine learning approach. Energy, 263, 125900. doi:10.1016/j.energy.2022.125900.

Park, J., & Kim, S. (2020). Machine Learning-Based Activity Pattern Classification Using Personal PM2.5 Exposure Information. International Journal of Environmental Research and Public Health, 17(18), 6573. doi:10.3390/ijerph17186573.

Ren, S., He, K., Girshick, R., & Sun, J. (2017). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6), 1137–1149. doi:10.1109/TPAMI.2016.2577031.

Oikarinen, E., Tiittanen, H., Henelius, A., & Puolamäki, K. (2021). Detecting virtual concept drift of regressors without ground truth values. Data Mining and Knowledge Discovery, 35(3), 726–747. doi:10.1007/s10618-021-00739-7.

Takacs, A., Toledano-Ayala, M., Dominguez-Gonzalez, A., Pastrana-Palma, A., Velazquez, D. T., Ramos, J. M., & Rivas-Araiza, E. A. (2020). Descriptor Generation and Optimization for a Specific Outdoor Environment. IEEE Access, 8, 52550–52565. doi:10.1109/ACCESS.2020.2975474.

Widmer, G., Kubat, M. (1993). Effective learning in dynamic environments by explicit context tracking. In: Brazdil, P.B. (eds.) Machine Learning: ECML-93. ECML 1993. Lecture Notes in Computer Science, vol 667. Springer, Berlin, Heidelberg. doi:10.1007/3-540-56602-3_139.

Hulten, G., Spencer, L., & Domingos, P. (2001). Mining time-changing data streams. Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. doi:10.1145/502512.502529.

Black, M., & Hickey, R. J. (1999). Maintaining the performance of a learned classifier under concept drift. Intelligent Data Analysis, 3(6), 453–474. doi:10.3233/IDA-1999-3604.

Jia, R., Dao, D., Wang, B., Hubis, F. A., Gurel, N. M., Li, B., ... & Song, D. (2019). Efficient task-specific data valuation for nearest neighbor algorithms. arXiv Preprint arXiv:1908.08619. doi:10.48550/arXiv.1908.08619.

Klinkenberg, R. (2001). Using labeled and unlabeled data to learn drifting concepts. Workshop notes of the IJCAI-01 Workshop on Learning from Temporal and Spatial Data, 16-24. Held in conjunction with the International Joint Conference on Artificial Intelligence (IJCAI): AAAI Press, 4-6 August, 2001, Menlo Park, United States.

Liu, Y., Liu, Y., Yu, B. X. B., Zhong, S., & Hu, Z. (2023). Noise-robust oversampling for imbalanced data classification. Pattern Recognition, 133. doi:10.1016/j.patcog.2022.109008.

Maletzke, A., Dos Reis, D., Cherman, E., & Batista, G. (2019). DyS: A Framework for Mixture Models in Quantification. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 4552–4560. doi:10.1609/aaai.v33i01.33014552.

Li, P., Wu, X., & Hu, X. (2012). Mining recurring concept drifts with limited labeled streaming data. ACM Transactions on Intelligent Systems and Technology, 3(2). doi:10.1145/2089094.2089105.

Djouzi, K., Beghdad-Bey, K., & Amamra, A. (2022). A new adaptive sampling algorithm for big data classification. Journal of Computational Science, 61, 101653. doi:10.1016/j.jocs.2022.101653.

Nan, L. (2013). Classification algorithm for data streams with concept drift and its applications. Master Thesis, Fujian Normal University, Minhou, China.

Wang, G., & Wang, Y. (2023). Self-attention network for few-shot learning based on nearest-neighbor algorithm. Machine Vision and Applications, 34(2), 28. doi:10.1007/s00138-023-01375-5.

Wu, Z., Efros, A. A., & Yu, S. X. (2018). Improving generalization via scalable neighborhood component analysis. In Proceedings of the european conference on computer vision (ECCV), 685-701. doi:10.48550/arXiv.1808.04699.

Maillo, J., Ramírez, S., Triguero, I., & Herrera, F. (2017). kNN-IS: An Iterative Spark-based design of the k-Nearest Neighbors classifier for big data. Knowledge-Based Systems, 117, 3–15. doi:10.1016/j.knosys.2016.06.012.

Deng, Z., Zhu, X., Cheng, D., Zong, M., & Zhang, S. (2016). Efficient KNN classification algorithm for big data. Neurocomputing, 195, 143–148. doi:10.1016/j.neucom.2015.08.112.

Ou, G., He, Y., Fournier-Viger, P., & Huang, J. Z. (2022). A Novel Mixed-Attribute Fusion-Based Naive Bayesian Classifier. Applied Sciences (Switzerland), 12(20), 10443. doi:10.3390/app122010443.

Karegowda, A. G., V, P., Jayaram, M. A., & Manjunath, A. S. (2012). Rule based Classification for Diabetic Patients using Cascaded K-Means and Decision Tree C4.5. International Journal of Computer Applications, 45(12), 45–50. doi:10.5120/6836-9460.

Khan, S., & Yairi, T. (2018). A review on the application of deep learning in system health management. Mechanical Systems and Signal Processing, 107, 241–265. doi:10.1016/j.ymssp.2017.11.024.

Maletzke, A. G., dos Reis, D. M., & Batista, G. E. A. P. A. (2018). Combining instance selection and self-training to improve data stream quantification. Journal of the Brazilian Computer Society, 24(1). doi:10.1186/s13173-018-0076-0.

Gama, J., Zliobaite, I., Bifet, A., Pechenizkiy, M., & Bouchachia, A. (2014). A survey on concept drift adaptation. ACM Computing Surveys, 46(4), 1–37. doi:10.1145/2523813.

Huang, D. T. J., Koh, Y. S., Dobbie, G., & Pears, R. (2014). Detecting Volatility Shift in Data Streams. 2014 IEEE International Conference on Data Mining. doi:10.1109/icdm.2014.50.

Zheng, X., Aragam, B., Ravikumar, P. K., & Xing, E. P. (2018). Dags with no tears: Continuous optimization for structure learning. 32nd Conference on Neural Information Processing Systems (NeurIPS 2018), Montréal, Canada.

Di Franco, G., & Santurro, M. (2021). Machine learning, artificial neural networks and social research. Quality and Quantity, 55(3), 1007–1025. doi:10.1007/s11135-020-01037-y.

Scanagatta, M., Corani, G., Zaffalon, M., Yoo, J., & Kang, U. (2018). Efficient learning of bounded-treewidth Bayesian networks from complete and incomplete data sets. International Journal of Approximate Reasoning, 95, 152–166. doi:10.1016/j.ijar.2018.02.004.

Trevizan, B., Chamby-Diaz, J., Bazzan, A. L. C., & Recamonde-Mendoza, M. (2020). A comparative evaluation of aggregation methods for machine learning over vertically partitioned data. Expert Systems with Applications, 152, 113406. doi:10.1016/j.eswa.2020.113406.

Guo, Y., Chen, Q., Chen J., Wu Q., Shi Q., Tan M. (2019). Auto-embedding generative adversarial networks for high resolution image synthesis. IEEE Transactions on Multimedia, 21(11), 2726-2737. doi:10.1109/TMM.2019.2908352.

Lee, M. H., Kim, N., Yoo, J., Kim, H. K., Son, Y. D., Kim, Y. B., Oh, S. M., Kim, S., Lee, H., Jeon, J. E., & Lee, Y. J. (2021). Multitask fMRI and machine learning approach improve prediction of differential brain activity pattern in patients with insomnia disorder. Scientific Reports, 11(1), 9402. doi:10.1038/s41598-021-88845-w.

Zhu, X. (2010). Power Supply dataset. Stream Data Mining Repository: Florida Atlantic University. Available online: http://www.cse.fau.edu/~xqzhu/stream.html (accessed on April 2023).

Kaggle (2023). Energy generation dataset. Available online: https://www.kaggle.com/nicholasjhana/energy-consumption-generation-prices-and-weather/data?select=energy_dataset.csv (accessed on April 2023).

UCI. (2023). Steel Industry Energy Consumption Dataset. Machine Learning Repository: Center for Machine Learning and Intelligent Systems. Available online: http://archive.ics.uci.edu/ml/datasets/Steel+Industry+Energy+Consumption+Dataset (accessed on April 2023).

UCI (2014). Combined Cycle Power Plant Data Set. Machine Learning Repository: Center for Machine Learning and Intelligent Systems https://archive.ics.uci.edu/ml/datasets/combined+cycle+power+plant# (accessed on April 2023).

Meiseles, A., & Rokach, L. (2020). Source Model Selection for Deep Learning in the Time Series Domain. IEEE Access, 8, 6190–6200. doi:10.1109/ACCESS.2019.2963742.

Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53–65. doi:10.1016/0377-0427(87)90125-7.


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DOI: 10.28991/ESJ-2023-07-03-03

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