Vassia Atanassova at 10:13, 15 December 2018

2018-12-15T10:13:30Z

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	\| abstract = We present a novel method of attribute selection for data bases which are expressed via intuitionistic fuzzy sets (IFSs, for short). We use the three term representation of the IFSs which makes it possible to construct a transparent and justified function that makes it possible to		\| abstract = We present a novel method of attribute selection for data bases which are expressed via intuitionistic fuzzy sets (IFSs, for short). We use the three term representation of the IFSs which makes it possible to construct a transparent and justified function that makes it possible to select attributes for widely understood decision making, e.g., for classification tasks. We test the proposed method using a well known example from literature. The results obtained are compared with other methods.
	select attributes for widely understood decision making, e.g., for classification tasks. We test the proposed method using a well known example from literature. The results obtained are compared with other methods.
	\| keywords = Intuitionistic fuzzy sets, Three term representation of IFSs, Selection of attributes.		\| keywords = Intuitionistic fuzzy sets, Three term representation of IFSs, Selection of attributes.
	\| ams = 03E72, 34Gxx.		\| ams = 03E72, 34Gxx.

Vassia Atanassova: Created page with "{{PAGENAME}} {{PAGENAME}} {{PAGENAME}}..."

2018-12-15T10:12:51Z

Created page with "{{PAGENAME}} {{PAGENAME}} {{PAGENAME}}..."

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[[Category:Publications on intuitionistic fuzzy sets|{{PAGENAME}}]]
[[Category:Publications in Notes on IFS|{{PAGENAME}}]]
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{{issue/title
| title = Selection of the attributes in intuitionistic fuzzy models
| shortcut = nifs/24/4/63-71
}}
{{issue/author
| author = Eulalia Szmidt
| institution = Systems Research Institute, Polish Academy of Sciences
| address = ul. Newelska 6, 01-447 Warsaw, Poland
| institution-2 = Warsaw School of Information Technology
| address-2 = ul. Newelska 6, 01-447 Warsaw, Poland
| email-before-at = szmidt
| email-after-at = ibspan.waw.pl
}}
{{issue/author
| author = Janusz Kacprzyk
| institution = Systems Research Institute, Polish Academy of Sciences
| address = ul. Newelska 6, 01-447 Warsaw, Poland
| institution-2 = Warsaw School of Information Technology
| address-2 = ul. Newelska 6, 01-447 Warsaw, Poland
| email-before-at = kacprzyk
| email-after-at = ibspan.waw.pl
}}
{{issue/data
| issue = [[Notes on Intuitionistic Fuzzy Sets/24/4|Notes on Intuitionistic Fuzzy Sets, Volume 24 (2018), Number 4]], pages 63–71
| doi = https://doi.org/10.7546/nifs.2018.24.4.63-71
| file = NIFS-24-4-063-071.pdf
| format = PDF
| size = 335 Kb
| abstract = We present a novel method of attribute selection for data bases which are expressed via intuitionistic fuzzy sets (IFSs, for short). We use the three term representation of the IFSs which makes it possible to construct a transparent and justified function that makes it possible to
select attributes for widely understood decision making, e.g., for classification tasks. We test the proposed method using a well known example from literature. The results obtained are compared with other methods.
| keywords = Intuitionistic fuzzy sets, Three term representation of IFSs, Selection of attributes.
| ams = 03E72, 34Gxx.
| references =

# Atanassov, K. (1983). Intuitionistic fuzzy sets. VII ITKR Session, Sofia 20–23 June, 1983 (Central Sci. and Techn. Library, Bulg. Academy of Sciences, 1984). (in Bulgarian). Reprinted: Int. J. Bioautomation, 2016, 20(S1), S1–S6 (in English).
# Atanassov, K. (1999). Intuitionistic Fuzzy Sets: Theory and Applications. Springer-Verlag.
# Atanassov, K. (2012). On Intuitionistic Fuzzy Sets Theory. Springer-Verlag.
# Atanassova, V. (2005). Strategies for decision making in the conditions of intuitionistic fuzziness. Reusch B. (eds) Computational Intelligence, Theory and Applications. Advances in Soft Computing, vol 33. Springer, Berlin, Heidelberg, 263–269.
# Baldwin, J. F., Lawry, J., & Martin, T. P. (1998). The Application of generalized Fuzzy Rules to Machine Learning and Automated Knowledge Discovery. Internationa Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 6(5), 459–487.
# Bujnowski, P., Szmidt, E., & Kacprzyk, J. (2014). Intuitionistic Fuzzy Decision Trees - a new Approach. Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., & Zurada, J. (Eds.): Artificial Intelligence and Soft Computing, Part I. Springer, Switzerland, 181–192.
# Bustince, H., Mohedano, V., Barrenechea, E., & Pagola, M. (2006). An algorithm for calculating the threshold of an image representing uncertainty through A-IFSs. Proc. of IPMU’2006, 2383–2390.
# Hellwig, Z. (1968). On the optimal choice of predictors. Study VI. Z. Gostkowski (ed.): Toward a system of quantitative indicators of components of human resources development; Paris: UNESCO.
# Jackson, J. E. (1991). A User’s Guide to Principal Components. New York: JohnWiley and Sons.
# Jolliffe, I. T. (1986). Principal Component Analysis. Springer-Verlag, 1986.
# Mardia, K. V., Kent, J. T., & Bibby, J. M. (1995). Multivariate Analysis. Probability and Mathematical Statistics. Academic Press.
# Roeva, O., & Michalikova, A. (2013). Generalized net model of intuitionistic fuzzy logic control of genetic algorithm parameters. Notes on Intuitionistic Fuzzy Sets, 19(2), 71–76.
# Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1, 81–106.
# Szmidt, E. (2014). Distances and Similarities in Intuitionistic Fuzzy Sets. Springer.
# Szmidt, E., & Baldwin, J. (2006). Intuitionistic Fuzzy Set Functions, Mass Assignment Theory, Possibility Theory and Histograms. Proc. of 2006 IEEE World Congress on Computational Intelligence, 237–243.
# Szmidt, E., & Kacprzyk, J. (1997). On measuring distances between intuitionistic fuzzy sets, Notes on Intuitionistic Fuzzy Sets, 3(4), 1–13.
# Szmidt, E., & Kacprzyk, J. (2000). Distances between intuitionistic fuzzy sets, Fuzzy Sets and Systems, 114(3), 505–518.
# Szmidt, E., & Kacprzyk, J. (2001). Entropy for intuitionistic fuzzy sets. Fuzzy Sets and Systems, 118 (3), 467–477.
# Szmidt, E. & Kacprzyk, J. (2006). Distances Between Intuitionistic Fuzzy Sets: Straightforward Approaches may not work. Proc. of IEEE Intelligent Systems’06, 716–721.
# Szmidt, E. & Kacprzyk, J. (2007). Some problems with entropy measures for the Atanassov intuitionistic fuzzy sets. Applications of Fuzzy Sets Theory. LNAI 4578, Springer-Verlag, 291–297.
# Szmidt, E. & Kacprzyk, J. (2007a). A New Similarity Measure for Intuitionistic Fuzzy Sets: Straightforward Approaches may not work. 2007 IEEE Conf. on Fuzzy Systems, 481–486.
# Szmidt, E. & Kacprzyk, J. (2010). Dealing with typical values via Atanassov’s intuitionistic fuzzy sets. Int. J. of General Systems, 39(5), 489–506.
# Szmidt, E. & Kacprzyk, J. (2012). A New Approach to Principal Component Analysis for Intuitionistic Fuzzy Data Sets. Greco, S. et al. (Eds.): IPMU 2012, Part II, CCIS 298, Springer-Verlag Berlin Heidelberg, 529–538.
# Szmidt, E. & Kacprzyk, J. (2018). A New Approach to Hellwig’s Method of Data Reduction for Atanassov’s Intuitionistic Fuzzy Sets. Medina, J. et al. (Eds.): IPMU 2018, CCIS 855, 553–564.
# Szmidt, E., & Kukier, M. (2006). Classification of Imbalanced and Overlapping Classes using Intuitionistic Fuzzy Sets. Proc. of IEEE Intelligent Systems’06, London, 722–727.
# Szmidt, E., & Kukier, M. (2008). A New Approach to Classification of Imbalanced Classes via Atanassov’s Intuitionistic Fuzzy Sets. Hsiao-Fan Wang (Ed.): Intelligent Data Analysis: Developing New Methodologies Through Pattern Discovery and Recovery. Idea Group, 85–101.
# Szmidt, E., & Kukier, M. (2008a). Atanassov’s intuitionistic fuzzy sets in classification of imbalanced and overlapping classes. Chountas, P., Petrounias, I., Kacprzyk, J. (Eds.): Intelligent Techniques and Tools for Novel System Architectures. Springer, Berlin Heidelberg 2008, 455–471.
# Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338–353.

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Issue:Selection of the attributes in intuitionistic fuzzy models - Revision history

Vassia Atanassova at 10:13, 15 December 2018

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