Доступ предоставлен для: Guest
Портал Begell Электронная Бибилиотека e-Книги Журналы Справочники и Сборники статей Коллекции
Journal of Automation and Information Sciences
SJR: 0.275 SNIP: 0.59 CiteScore™: 0.8

ISSN Печать: 1064-2315
ISSN Онлайн: 2163-9337

Выпуски:
Том 52, 2020 Том 51, 2019 Том 50, 2018 Том 49, 2017 Том 48, 2016 Том 47, 2015 Том 46, 2014 Том 45, 2013 Том 44, 2012 Том 43, 2011 Том 42, 2010 Том 41, 2009 Том 40, 2008 Том 39, 2007 Том 38, 2006 Том 37, 2005 Том 36, 2004 Том 35, 2003 Том 34, 2002 Том 33, 2001 Том 32, 2000 Том 31, 1999 Том 30, 1998 Том 29, 1997 Том 28, 1996

Journal of Automation and Information Sciences

DOI: 10.1615/JAutomatInfScien.v51.i5.50
pages 54-64

Information Technology of Separating Hyperplanes Synthesis for Linear Classifiers

Alexander V. Barmak
Khmelnitskiy National University, Khmelnitskiy
Yuriy V. Krak
Kiev National Taras Shevchenko University, V.M. Glushkov Institute of Cybernetics of National Academy of Sciences of Ukraine, Kiev
Eduard A. Manziuk
Khmelnitskiy National University, Khmelnitskiy
Veda S. Kasianiuk
Kiev National Taras Shevchenko University, Kiev

Краткое описание

Information technology allowing one to implement the tasks of classification, clustering, studying the topology of the data of the information component is proposed. The multidimensional feature space is reduced to the visual presentation space to determine the information content of the data. Optimized reduction of the space dimension to two-dimensional one applying multidimensional scaling methods is used. Visual definition of grouping data allows separating areas to form. The next stage is visual limitation of categories of classes using graphic separators. To enable flexibility of nonlinear areas limitation a combination of linear ones is used, thereby forming a piecewise linear set with necessary degree of sampling. Using piecewise linear constraints allows us to implement projecting into original multidimensional feature space. Visual construction of restrictive separators makes it possible to consider tolerance fields of changing of features parameters, separation measure of classes, nonlinearity of data grouping. This is followed by reverse expansion of space with the projection of the separators into n -dimensional space with the separating hyperspace synthesis. Thus the limitative areas of hyperspace for the necessary categories of classes are formed. At the same time the visualization of classification process in a hyperspace is provided. The information technology base is the multidimensional space projection into visual (two-dimensional) space construction piecewise linear limiters of studied areas, subsequent limiters projecting into multidimensional space. Thus the information technology enables us to synthesize separating hyperplanes limiting categories of classes in multidimensional space. The technology application successive stages are described.

ЛИТЕРАТУРА

  1. Ivakhnenko A.G., Self-organizing recognition and automatic control systems [in Russian], Tekhnika, Kiev, 1969.

  2. Vapnik V.N., Statistical learning theory, Wiley, New York, 1998.

  3. Kirichenko M.F., KrakYu.V., Polishchuk A.A., Pseudo inverse and projective matrices in problems of synthesis of functional transformers, Kibernetika i sistemnyj analiz, 2004,40, No. 3, 116-129.

  4. Cox T.F., Cox M.A.A., Multidimensional scaling, 2nd ed., Chapman and Hall. CRC, 2001.

  5. Krak Iu.V., Kudin G.I., Kulias A.I., Multidimensional scaling by means of pseudoinverse operations, Cybernetics and Systems Analysis, 2019, 55, No. 1, 22-29.

  6. Barmak O., Krak Y., Manziuk E., Characteristics for choice of models in the ensembles, Proceedings of the 11th International Conference of Programming UkrPROG 2018, Kyiv, Ukraine, May 22-24, 2018, 2139, 171-179.

  7. Manziuk E.A., Barmak O.V., Krak Iu.V., Kasianiuk V.S., Definition of information core for documents classification, Journal of Automation and Information Sciences, 2018, 50, No. 4, 25-34.

  8. MairP., BorgL, RuschT., Goodness-of-fit assessment in multidimensional scaling and unfolding, Multivariate Behavioral Research, 2016, 51, No. 6, 772-789.

  9. Stojkoska B.R., A taxonomy of localization techniques based on multidimensional scaling, 39th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija, 2016, 649-654.

  10. Leeuw J. de, Mair P., Multidimensional scaling using majorization: SMACOF in R, Journal of Statistical Software, 2009, 31, No. 3, 1-30.

  11. Guttman L., A general nonmetric technique for finding the smallest coordinate space for a configuration of points, Psychometrics, 1968, 33, No. 4, 469-506.

  12. Krak I.V., Kryvonos I.G., Barmak O.V., Ternov A.S., An approach to the determination of efficient features and synthesis of an optimal band-separating classifier of dactyl elements of sign language, Cybernetics and Systems Analysis, 2016, 52, No. 2, 173-180.

  13. Kryvonos I.G., Krak I.V., Barmak O.V., Shkilniuk D.V., Construction and identification of elements of sign communication, Cybernetics and Systems Analysis, 2013, 49, No. 2, 163-172.

  14. Kruskal J.B., Wish M., Multidimensional scaling, sage university paper series on quantitative application in the social sciences, 07-011, Sage Publications. Multidimensional Scaling, Beverly Hills, London, 1978.

  15. Quinlan J.R., C4.5: Programs for machine learning, Morgan Kaufmann Publishers Inc., San Mateo, 1993.


Articles with similar content:

Recommendations on Practical Implementation of the Algorithm for Automatic Processing of Facsimile Reports in Document Circulation Systems
Telecommunications and Radio Engineering, Vol.68, 2009, issue 2
A. A. Minyaev
On the Fenchel-Moreau Duality in a Differential Game of Several Players with the Terminal Payoff Function
Journal of Automation and Information Sciences, Vol.29, 1997, issue 1
Iosif S. Rappoport
Survey of Spectral Methods of Embedding of Watermarks into Audio Signals
Journal of Automation and Information Sciences, Vol.42, 2010, issue 10
Natalya V. Koshkina
R-Functions and Atomic Functions in Problems of Digital Processing of Multivariate Signals
Telecommunications and Radio Engineering, Vol.56, 2001, issue 4&5
Miklhail Alekseevich Basarab, Victor Filippovich Kravchenko
IMAGE CLASSIFICATION METHODS IN THE SPACE OF DESCRIPTIONS IN THE FORM OF A SET OF THE KEY POINT DESCRIPTORS
Telecommunications and Radio Engineering, Vol.77, 2018, issue 9
Volodymyr A. Gorokhovatskyi