Instance selection techniques in reduction of data streams derived from medical devices

• Autorzy: Byczkowska-Lipińska L. , Wosiak A.

Identyfikatory

DOI

10.15199/48.2017.12.29

Warianty tytułu

PL

Redukcja strumienia danych pozyskiwanych z urządzeń diagnostyki medycznej za pomocą technik selekcji przypadków

• Języki publikacji: EN

Abstrakty

EN

The research described in this paper concerns the reduction of streams of data derived from medical devices, i.e. ECG recordings. Experimental studies included three instance selection techniques: thresholding method, bounds checking and frequent data reduction . It was shown that application the instance selection techniques may reduce data stream by over 90% without losing anomalies or the measurements that are key values for the medical diagnosis.

PL

W ramach niniejszej pracy przeprowadzona została redukcja strumienia danych pozyskanych z urządzeń medycznych. Badania eksperymentalne obejmowały zastosowanie trzech technik selekcji przypadków: metody eliminacji progowej, weryfikacji zakresu oraz redukcji obiektów częstych. W pracy zostało wykazane, że zastosowanie selekcji przypadków pozwala na redukcję strumienia danych o ponad 90% bez utraty wartości kluczowych dla postawienia diagnozy medycznej.

Słowa kluczowe

EN

instance selection; data stream; medical data analysis

PL

selekcja przypadków; strumień danych; analiza danych medycznych

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2017
• Tom: R. 93, nr 12
• Strony: 115--118
• Opis fizyczny: Bibliogr. 38 poz., rys., tab., wykr.

Twórcy

autor

Byczkowska-Lipińska L.

• University of Computer Sciences and Skills, ul. Rzgowska 17 a, 93-008 Lodz, Poland

autor

Wosiak A.

• Lodz University of Technology, Institute of Information Technology, ul. Wólczańska 215, 90-924 Lodz

Bibliografia

• [1] Bellman, R. (2013). Dynamic programming. Courier Corporation.
• [2] Bellman, R. E. (2015). Adaptive control processes: a guided tour. Princeton University Press.
• [3] Keogh, E., Mueen, A. (2011). Curse of dimensionality. In Encyclopedia of Machine Learning (pp. 257-258). Springer US.
• [4] Chen, L. (2009). Curse of dimensionality. In Encyclopedia of Database Systems (pp. 545-546). Springer US.
• [5] Abdi, H., Williams, L. J. (2010). Principal component analysis. Wiley interdisciplinary reviews: computational statistics, 2(4), pp. 433-459.
• [6] Gorban, A. N., Kégl, B., Wunsch, D. C., & Zinovyev, A. Y. (Eds.). (2008). Principal manifolds for data visualization and dimension reduction, Vol. 58, pp. 96-130. Berlin-Heidelberg: Springer.
• [7] Byczkowska-Lipińska L., Wosiak A. (2015). Feature Selection and Classification Techniques in the Assessment of the State for Large Power Transformers. Przegląd Elektrotechniczny, R. 91 NR 1/2015, doi:10.15199/48.2015.01.39
• [8] Liu L., Özsu M. T. (Eds.) (2009). Encyclopedia of database systems. Berlin, Heidelberg, Germany: Springer. De
• [9] Choudhury, M., Lin, Y. R., Sundaram, H., Candan, K. S., Xie, L., & Kelliher, A. (2010). How does the data sampling strategy impact the discovery of information diffusion in social media?. ICWSM, 10, pp. 34-41.
• [10] Holmes, A. (2012). Hadoop in practice. Manning Publications Co..
• [11] Buza K., Nanopoulos A., Schmidt-Thieme L., Koller J. (2011, July). Fast classification of electrocardiograph signals via instance selection. In Healthcare Informatics, Imaging and Systems Biology (HISB), 2011 First IEEE International Conference on, pp. 9 – 16.
• [12] Ramírez-Gallego S., Krawczyk B., García S., Woźniak M., Herrera F. (2017). A survey on data preprocessing for data stream mining: Current status and future directions. Neurocomputing, 239, pp. 39 – 57.
• [13] García, S., Ramírez-Gallego, S., Luengo, J., Benítez, J. M., Herrera, F. (2016). Big data preprocessing: methods and prospects. Big Data Analytics, 1(1), 9.
• [14] Pyle, D. (1999). Data preparation for data mining (Vol. 1). Morgan Kaufmann.
• [15] Hall, M. A. (1999). Correlation-based feature selection for machine learning.
• [16] Guyon, I., Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of Machine Learning Research, vol. 3, pp. 1157-1182.
• [17] Chandrashekar, G., & Sahin, F. (2014). A survey on feature selection methods. Computers & Electrical Engineering, 40(1), 16-28.
• [18] Giráldez, R. (2005, June). Feature influence for evolutionary learning. In Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation, pp. 1139-1145. ACM.
• [19] Kim, J. O., Mueller, C. W. (1978). Factor analysis: Statistical methods and practical issues, Vol. 14. Sage.
• [20] Dunteman, G. H. (1989). Principal components analysis. Vol. 69. Sage.
• [21] Smith, L. I. (2002). A tutorial on principal components analysis. Cornell University, USA, vol. 51(52), no 65.
• [22] Groth, D., Hartmann, S., Klie, S., Selbig, J. (2013). Principal components analysis. Computational Toxicology: Volume II, pp. 527-547.
• [23] Bressan, M., Vitria, J. (2003). On the selection and classification of independent features. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(10), pp. 1312- 1317.
• [24] Liu, H., Motoda, H. (2002). On issues of instance selection. Data Mining and Knowledge Discovery, 6(2), pp. 115-130.
• [25] Olvera-López, J. A., Carrasco-Ochoa, J. A., Martínez-Trinidad, J. F., & Kittler, J. (2010). A review of instance selection methods. Artificial Intelligence Review, vol. 34(2), pp. 133- 143.
• [26] Garcia, S., Derrac, J., Cano, J., & Herrera, F. (2012). Prototype selection for nearest neighbor classification: Taxonomy and empirical study. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34(3), pp. 417-435.
• [27] García, S., Luengo, J., Herrera, F. (2015). Data preprocessing in data mining (pp. 59-139). New York: Springer.
• [28] Garcia, S., Derrac, J., Cano, J., & Herrera, F. (2012). Prototype selection for nearest neighbor classification: Taxonomy and empirical study. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34(3), pp. 417-435.
• [29] Aha, D. W., Kibler, D., & Albert, M. K. (1991). Instance-based learning algorithms. Machine learning, vol. 6(1), pp. 37-66.
• [30] Salganicoff, M. (1993, December). Density-adaptive learning and forgetting. In Proceedings of the Tenth International Conference on Machine Learning (Vol. 3, pp. 276-283).
• [31] Klinkenberg, R. (2004). Learning drifting concepts: Example selection vs. example weighting. Intelligent Data Analysis, vol. 8(3), pp. 281-300.
• [33] Brighton, H., & Mellish, C. (2002). Advances in instance selection for instance-based learning algorithms. Data mining and knowledge discovery, vol. 6(2), pp. 153-172.
• [34] Goldberger A.L., Amaral L.A.N., Glass L., Hausdorff J.M., Ivanov P.Ch., Mark R.G., Mietus J.E., Moody G.B., Peng C.K., Stanley H.E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation vol. 101(23), pp. e215-e220, DOI: 10.1161/01.CIR.101.23.e215
• [35] Goldsmith R.L., Bigger J.T., Bloomfield D.M., Krum H., Steinman R.C., Sackner-Bernstein J., Packer M. Long-term carvedilol therapy increases parasympathetic nervous system activity in chronic congestive heart failure. American Journal of Cardiology 1997; vol. 80, pp. 1101-1104.
• [36] Olvera-López, J. A., Carrasco-Ochoa, J. A., Martínez-Trinidad, J. F., & Kittler, J. (2010). A review of instance selection methods. Artificial Intelligence Review, vol. 34(2), pp. 133- 143.
• [37] Witten I. H., Frank E., Hall M. A., Pal C. J. (2017). Data Mining: Practical machine learning tools and techniques. Fourth Edition. Morgan Kaufmann.
• [38] Guo, L., Chen, F., Gao, C., & Xiong, W. (2012). Performance Measurement Model of Multi-Source Data Fusion Based on Network Situation Awareness. Przegląd Elektrotechniczny, vol. 88(7b), pp. 315-319.

Uwagi

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).