Algorithm for Constructing a Classifier Team Using a Modified PCA (Principal Component Analysis) in the Task of Diagnosis of Acute Lymphocytic Leukaemia Type B-CLL | SpringerLink
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Algorithm for Constructing a Classifier Team Using a Modified PCA (Principal Component Analysis) in the Task of Diagnosis of Acute Lymphocytic Leukaemia Type B-CLL

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Hybrid Artificial Intelligent Systems (HAIS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11734))

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Abstract

Systems of data recognition and data classification are getting more and more developed. There appear newer algorithms that solve more difficult and complex decision problems. Very good results are obtained using sets of classifiers. The authors in their research focused on certain data characteristics. The characteristics concerns recognition of classes of objects whose features can be grouped. Clusters created in this manner can contribute to better recognition of certain decision classes. One such example is a diagnosis of forecast in the case of acute lymphocytic chronic leukaemia B-CLL type. In this document, the authors present a modified selection method of features of the PCA object. The modification concerns the rotation of objects in relation to decision classes. In addition to grouping similar features using Varimax rotation, a procedure for grouping patients in these PCA groups was developed. Within each PCA, two classifiers - strong and weak ones were built. In the research part, the developed method was compared to the one-stage recognition algorithms known from the literature. The obtained results have a significant contribution to medical diagnostics. They allow to develop a procedure for treatment of B-CLL lymphocytic leukaemia. Making an appropriate diagnosis allows to increase a patient’s survival chance by implementing appropriate treatment.

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References

  1. Burduk, R.: Integration base classifiers based on their decision boundary. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2017. LNCS (LNAI), vol. 10246, pp. 13–20. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59060-8_2

    Chapter  Google Scholar 

  2. Woźniak, M., Ksieniewicz, P., Cyganek, B., Kasprzak, A., Walkowiak, K.: Active learning classification of drifted streaming data. Procedia Comput. Sci. 80, 1724–1733 (2014)

    Article  Google Scholar 

  3. Krawczyk, B., Ksieniewicz, P., Woźniak, M.: Hyperspectral image analysis based on color channels and ensemble classifier. In: Polycarpou, M., de Carvalho, A.C.P.L.F., Pan, J.-S., Woźniak, M., Quintian, H., Corchado, E. (eds.) HAIS 2014. LNCS (LNAI), vol. 8480, pp. 274–284. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07617-1_25

    Chapter  Google Scholar 

  4. Zyblewski, P., Ksieniewicz, P., Woźniak, M.: Classifier selection for highly imbalanced data streams with Minority Driven Ensemble. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds.) ICAISC 2019. LNCS (LNAI), vol. 11508, pp. 626–635. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20912-4_57

    Chapter  Google Scholar 

  5. Kay, N., Hamblin, T., Jelinek, D., et al.: Chronic lymphocytic leukemia. American Society of Hematology, Hematology, pp. 193–213 (2002)

    Article  MathSciNet  Google Scholar 

  6. Dmoszyńska, A., Robak, T.: Podstawy hematologii. Wydawnictwo Czelej, Lublin, wyd 2 (2008)

    Google Scholar 

  7. Hallek, M., Cheson, B., Catovsky, D., Caligaris-Cappio, F., Dighiero, G.: Guidelines for the diagnosis and treatment of chronic lymphocytic leukemia: a report from the International Workshop on Chronic Lymphocytic Leukemia (IWCLL) updating the National Cancer Institute-Working Group (NCI-WG) 1996 guidelines, vol. 111, pp. 5446–5456 (2008)

    Google Scholar 

  8. Monserrat, E., Gine, E., Bosch, F.: Redefining prognostic elements in chronic lymphocytic leukemia. Hematol J. 4(suppl. 3), 180–182 (2003)

    Google Scholar 

  9. Hamblin, T.J.: CLL: How many diseases? Hematol J. 4(suppl. 3), 183–186 (2003)

    Google Scholar 

  10. Rai, K.R., Chiorazzi, N.: Determining the clinical course and outcome in chronic lymphocytic leukemia. N. Engl. J. Med. 348, 1797–1799 (2003)

    Article  Google Scholar 

  11. Bosch, F., Villamor, N.: ZAP-70 expression in chronic lymphocytic leukemia: a new parameter for an old disease. Hematologica 88, 724–726 (2003)

    Google Scholar 

  12. Brugiatelli, M., Mannina, D., Neri, S., et al.: Recent update of prognosis and staging of chronic lymphocytic leukemia. Hematol J. 88(suppl. 10), 30–31 (2003)

    Google Scholar 

  13. Grabiński, T.: Metody taksonometrii. Akademia Ekonomiczna, Kraków (1992)

    Google Scholar 

  14. Stanisz, A.: Przystępny kurs statystyki z zastosowaniem Statistica PL na przykładach z medycyny. T. 3: Analizy wielowymiarowe. StatSoft, Kraków (2007)

    Google Scholar 

  15. Fix, E., Hodges, J.L.: Discriminatory analysis. Nonparametric discrimination: consistency properties, Report Number 4, Project Number 21–49-004, 1951, Reprinted in International Statistical Review, 57, pp. 238–247 (1989)

    MATH  Google Scholar 

  16. Fukunaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn. Academic Press, New York (1990)

    MATH  Google Scholar 

  17. Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995). https://doi.org/10.1007/978-1-4757-3264-1

    Book  MATH  Google Scholar 

  18. Quinlan, J.R.: Discovering rules by induction from large collections of examples. In: Expert Systems in the Micro Electronic Age, pp. 168–201. Edinburgh University Press (1979)

    Google Scholar 

  19. Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees, Wadsworth, Belmont (1984)

    Google Scholar 

  20. Keller, J.M., Gray, M.R., Givens, J.A.: A fuzzy K-nearest neighbor algorithm. IEEE Trans. Syst. Man Cybern. 15(4), 580–585 (1985)

    Article  Google Scholar 

  21. Devijver, P.A., Kittler, J.: Pattern Recognition: A Statistical Approach. Prentice-Hall, London (1982)

    MATH  Google Scholar 

  22. Sutton, C., McCallum, A.: An introduction to conditional random fields for relational learning. In: Getoor, L., Taskar, B. (eds.) Introduction to Statistical Relational Learning, pp. 93–128. MIT Press, Cambridge (2006)

    Google Scholar 

  23. Zhang, J., Gong, S.: Action categorization with modified hidden conditional random field. Pattern Recogn. 43, 197–203 (2010)

    Article  Google Scholar 

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Acknowledgement

This work was supported by the statutory funds of the Department of Systems and Computer Networks, Faculty of Electronics, Wroclaw University of Science and Technology.

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Authors and Affiliations

  1. Department of Systems and Computer Networks, Faculty of Electronics, Wroclaw University of Science and Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland

    Mariusz Topolski

  2. WSB Universities, Fabryczna 29/31, 53-609, Wroclaw, Poland

    Katarzyna Topolska

Authors
  1. Mariusz Topolski
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  2. Katarzyna Topolska
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Corresponding author

Correspondence to Katarzyna Topolska .

Editor information

Editors and Affiliations

  1. University of León, León, Spain

    Hilde Pérez García

  2. University of León, León, Spain

    Lidia Sánchez González

  3. University of León, León, Spain

    Manuel Castejón Limas

  4. University of A Coruña, Ferrol, Spain

    Héctor Quintián Pardo

  5. University of Salamanca, Salamanca, Spain

    Emilio Corchado Rodríguez

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Topolski, M., Topolska, K. (2019). Algorithm for Constructing a Classifier Team Using a Modified PCA (Principal Component Analysis) in the Task of Diagnosis of Acute Lymphocytic Leukaemia Type B-CLL. In: Pérez García, H., Sánchez González, L., Castejón Limas, M., Quintián Pardo, H., Corchado Rodríguez, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2019. Lecture Notes in Computer Science(), vol 11734. Springer, Cham. https://doi.org/10.1007/978-3-030-29859-3_52

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  • DOI: https://doi.org/10.1007/978-3-030-29859-3_52

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