Use of the mini-model method in classification task on example of iris flower dataset

• Autorzy: Pietrzykowski M.
• Języki publikacji: EN

Abstrakty

EN

The paper presents use of the mini-models method in a classification task. The article briefly describes the method and compares it to the k-nearest neighbor algorithm. The algorithm concentrates only on local query data and uses a data samples only from local neighborhood of the query. The paper presents the results of experiment that compare the effectiveness of mini-models with selected methods of classification. The experiments were performed on well-known Iris Flower dataset and on other popular classification datasets.

Słowa kluczowe

EN

mini-model; local classification; k-nearest neighbors method; mathematical modeling

• Wydawca: Komisja Informatyki Polskiej Akademii Nauk, Oddział w Gdańsku
• Czasopismo: Journal of Theoretical and Applied Computer Science
• Rocznik: 2014
• Tom: Vol. 8, nr 2
• Strony: 27--36
• Opis fizyczny: Bibliogr. 18 poz., rys., tab.

Twórcy

autor

Pietrzykowski M.

• Faculty of Computer Science and Information Technology, West Pomeranian University of Technology, Szczecin, Poland

Bibliografia

• [1] Piegat A., Wąsikowska B., Korzeń M.: Application of the self-learning, 3-point mini-model for modelling of unemployment rate in Poland. Studia Informatica, nr 27, University of Szczecin, 2010, pp. 59-69, [in Polish].
• [2] Piegat A., Wąsikowska B., Korzeń M.: Differences between the method of mini-models and the k-nearest neighbors an example of modeling unemployment rate in Poland. Information Systems in management IX-Business Intelligence and Knowledge Management. WULS Press, Warsaw, 2011, pp. 34-43.
• [3] Pietrzykowski M.: The use of linear and nonlinear mini-models in process of data modelling in a 2D-space.Nowe Trendy w Naukach Inżynieryjnych. CREATIVETIME, Kraków, 2011, pp. 100-108.
• [4] Pietrzykowski M.: Comparison of effectiveness of linear mini-models with some methods of modelling. Młodzi Naukowcy dla Polskiej Nauki. CREATIVETIME, Kraków, 2011, pp. 113-123.
• [5] Pietrzykowski M.: Effectiveness of mini-models method when data modelling within a 2Dspace in an information deficiency situation. Journal of Theoretical and Applied Computer Science, vol. 6, no. 3, 2012, pp. 21-27.
• [6] Pluciński M.: Mini-models - Local Regression Models for the Function Approximation Learning. In Rutkowski L. et. al. Proceedings of ICAISC 2012, Part II, LNCS 7268, Springer-Verlag Berlin Heidelberg, 2012, pp. 160-167.
• [7] Pluciński M.: Nonlinear ellipsoidal mini-models - application for the function approximation task. Przegląd Elektrotechniczny (Electrical Review), R. 88 NR 10b/2012, pp. 247 - 251.
• [8] Pietrzykowski M.: Mini-models working in 3D space based on polar coordinate system. Nowe trendy w naukach inżynieryjnych 4. Tom II, CREATIVETIME, Kraków 2013, pp. 117-125.
• [9] Bronshtein I., Semendyayev K., Musiol G., Muhlig H.: Handbook of Mathematics. Springer, 2007. ISBN 9783540721215.
• [10] Polyanin A., Manzhirov A.: Handbook of Mathematics for Engineers and Scientists. Taylor & Francis, 2010. ISBN 9781584885023.
• [11] Moon P., Spencer D.: Field theory handbook: including coordinate systems, differential equations, and their solutions. Springer-Verlag, 1988. ISBN 9780387027326.
• [12] Fix E., Hodges J. L., Discriminatory analysis, nonparametric discrimination: Consistency properties, Randolph Field, Texas, 1951, pp. 1-21.
• [13] Kordos M., Blachnik M., Strzempa D.: Do We Need Whatever More than k-NN?, in Proceedings of 10-th International Conference on Artificial Inteligence and Soft Computing, Zakopane, 2010.
• [14] Fisher R.A.: The use of multiple measurments in taxonomic problems. Annals of Eugenics, vol. 7 (2), 1936, pp. 179–188.
• [15] Ben-Hur A., Horn D., Siegelmann H.T., Vapnik V.: Support vector clustering. Journal of Machine Learning Research, vol. 2, 2001, pp. 125-137.
• [16] Ster B., Dobnikar A.: Neural Networks in medical diagnosis: Comparison with other methods, EANN ’96, 1996, pp. 427-430.
• [17] Zarndt F.: A comprehensive case study: An examination of machine learning and connectionists algorithms, MSc Thesis, Department of Computer Science, Brigham Young University, 1995.
• [18] UCI Machine Learning Repository: http://archive.ics.uci.edu/ml/