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Advances in Feature Selection for Data and Pattern Recognition pp 163–182Cite as

Feature Selection Approach for Rule-Based Knowledge Bases

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Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 138))

Abstract

The subject-matter of this study is knowledge representation in rule-based knowledge bases. The two following issues will be discussed herein: feature selection as a part of mining knowledge bases from a knowledge engineer’s perspective (it is usually aimed at completeness analysis, consistency of the knowledge base and detection of redundancy and unusual rules) as well as from a domain expert’s point of view (domain expert intends to explore the rules with regard to their optimization, improved interpretation and a view to improve the quality of knowledge recorded in the rules). In this sense, exploration of rules, in order to select the most important knowledge, is based, in a great extent, on the analysis of similarities across the rules and their clusters. Building the representatives for created clusters of rules bases on the analysis of the left-hand sides of this rules and then selection of the best descriptive once. Thus we may treat this approach as a feature selection process.

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References

  1. Bazan, J.G., Szczuka, M.S., Wróblewski, J.: A new version of rough set exploration system. In: Alpigini, J.J., Peters, J.F., Skowron, A., Zhong, N. (eds.) Rough Sets and Current Trends in Computing. Lecture Notes in Computer Science 2475, Springer, Berlin, Heidelberg, pp. 397–404 (2002)

    Google Scholar 

  2. Boriah, S., Chandola, V., Kumar, V.: Similarity measures for categorical data: A comparative evaluation. Proceedings of the 8th SIAM International Conference on Data Mining. pp. 243–254, (2008)

    Google Scholar 

  3. Finch, H.: Comparison of distance measures in cluster. analysis with dichotomous data. Journal of Data Science 3. Ball State University. pp. 88–100 (2005)

    Google Scholar 

  4. Goodall, D.: A new similarity index based on probability. Biometrics. 22, 882–907 (1966)

    Article  Google Scholar 

  5. Gower, J.: A general coefficient of similarity and some of its properties. Biometrics. 27, 857–871 (1971)

    Article  Google Scholar 

  6. Guyon, I.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)

    MATH  Google Scholar 

  7. Jain, A.K., Dubes, R.C.: Algorithms for clustering data. Prentice Hall, New Jersey (1988)

    MATH  Google Scholar 

  8. Kumar, V., Minz, S.: Feature selection: A literature review. Smart Comput. Rev. 4(3), 211–229 (2014)

    Article  Google Scholar 

  9. Latkowski, R., Mikołajczyk, M.: Data decomposition and decision rule joining for classification of data with missing values. Lecture Notes in Artificial Intelligence 3066, Springer Verlag, pp. 254–263 (2004)

    Google Scholar 

  10. Lee, O., Gray, P.: Knowledge base clustering for kbs maintenance. Lecture Notes in Artificial Intelligence 3066, Springer Verlag. Journal of Software Maintenance and Evolution 10(6), 395–414 (1998)

    Google Scholar 

  11. Lichman, M.: Uci machine learning repository (2013). http://archive.ics.uci.edu/ml

  12. Nalepa, G., Ligeza, A., Kaczor, K.: Overview of knowledge formalization with XTT2 rules. Rule-Based Reasoning, Programming, and Applications, LNCS 6826, Springer Verlag. pp. 329–336 (2011)

    Google Scholar 

  13. Nowak-Brzezińska, A.: Mining rule-based knowledge bases inspired by rough set theory. Fundam. Inform. 148, 35–50 (2016)

    Article  MATH  MathSciNet  Google Scholar 

  14. Nowak-Brzezińska, A., Rybotycki, T.: Visualization of medical rule-based knowledge bases. J. Med. Inform. Technol. 24, 91–98 (2015)

    Google Scholar 

  15. Pindur, R., Susmaga, R., Stefanowski, J.: Hyperplane aggregation of dominance decision rules. Fundam. Inform. 61, 117–137 (2004)

    MATH  MathSciNet  Google Scholar 

  16. Sikora, M., Gudyś, A.: Chira-convex hull based iterative algorithm of rules aggregation. Fundam. Inform. 123, 143–170 (2013)

    MATH  MathSciNet  Google Scholar 

  17. Stefanowski, J., Weiss, D.: Comprehensible and accurate cluster labels in text clustering. In: Large Scale Semantic Access to Content (Text, Image, Video, and Sound), RIAO ’07, pp. 198–209 (2007)

    Google Scholar 

  18. Toivonen, H., Klemettinen, M., Ronkainen, P., Hatonen, K., Mannila, H.: Pruning and grouping discovered association rules. Workshop Notes of ECML Workshop on Statistics, Machine Learning, and Knowledge Discovery in Databases. pp. 47–52 (1995)

    Google Scholar 

  19. Wierzchoń, S., Kłopotek, M.: Algorithms of Cluster Analysis. IPI PAN, Warsaw, Poland (2015)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Institute of Computer Science, University of Silesia in Katowice, Będzińska 39, 41-200, Sosnowiec, Poland

    Agnieszka Nowak-Brzezińska

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  1. Agnieszka Nowak-Brzezińska
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Correspondence to Agnieszka Nowak-Brzezińska .

Editor information

Editors and Affiliations

  1. Silesian University of Technology , Gliwice, Poland

    Urszula Stańczyk

  2. University of Silesia in Katowice , Katowice, Poland

    Beata Zielosko

  3. University of Bournemouth , Poole, United Kingdom

    Lakhmi C. Jain

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Nowak-Brzezińska, A. (2018). Feature Selection Approach for Rule-Based Knowledge Bases. In: Stańczyk, U., Zielosko, B., Jain, L. (eds) Advances in Feature Selection for Data and Pattern Recognition. Intelligent Systems Reference Library, vol 138. Springer, Cham. https://doi.org/10.1007/978-3-319-67588-6_9

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  • DOI: https://doi.org/10.1007/978-3-319-67588-6_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67587-9

  • Online ISBN: 978-3-319-67588-6

  • eBook Packages: EngineeringEngineering (R0)

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