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Tytuł artykułu

Relationships Between Length and Coverage of Decision Rules

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Języki publikacji
EN
Abstrakty
EN
The paper describes a new tool for study relationships between length and coverage of exact decision rules. This tool is based on dynamic programming approach. We also present results of experiments with decision tables from UCI Machine Learning Repository.
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1--13
Opis fizyczny
Bibliogr. 26 poz., tab., wykr.
Twórcy
autor
  • Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia
autor
  • Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia
autor
  • Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia
autor
  • Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia
Bibliografia
  • [1] Alkhalid, A., Amin, T., Chikalov, I., Hussain, S.,Moshkov,M., Zielosko, B.: Dagger: A tool for analysis and optimization of decision trees and rules, in: Computational Informatics, Social Factors and New Information Technologies: Hypermedia Perspectives and Avant-Garde Experiences in the Era of Communicability Expansion, Blue Herons, 2011, 29–39.
  • [2] Amin, T., Chikalov, I., Moshkov, M., Zielosko, B.: Dynamic programming algorithm for optimization of _-decision rules, CS&P (M. Szczuka, L. Czaja, A. Skowron, M. Kacprzak, Eds.), Białystok University of Technology, 2011.
  • [3] Amin, T., Chikalov, I., Moshkov,M., Zielosko, B.: Dynamic programming approach for partial decision rule optimization, Fundam. Inform., 119(3-4), 2012, 233–248.
  • [4] Amin, T., Chikalov, I., Moshkov, M., Zielosko, B.: Relationships between length and coverage of exact decision rules, CS&P (L. Popova-Zeugmann, Ed.), CEUR-WS.org, 2012.
  • [5] Amin, T., Chikalov, I., Moshkov, M., Zielosko, B.: Dynamic programming approach for exact decision rule optimization, in: Rough Sets and Intelligent Systems - Professor Zdzisław Pawlak in Memoriam (A. Skowron, Z. Suraj, Eds.), vol. 42 of Intelligent Systems Reference Library, Springer, 2013, 211–228.
  • [6] Amin, T., Chikalov, I., Moshkov, M., Zielosko, B.: Dynamic programming approach to optimization of approximate decision rules, Inf. Sci., 221, 2013, 403–418.
  • [7] An, A., Cercone, N.: ELEM2: a learning system for more accurate classifications, Proceedings of the12th Biennial Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence, Springer-Verlag, London, UK, 1998.
  • [8] Asuncion, A., Newman, D. J.: UCI Machine Learning Repository, http://www.ics.uci.edu/~mlearn/,2007.
  • [9] Bazan, J. G., Szczuka, M. S., Wojna, A.,Wojnarski, M.: On the evolution of Rough Set Exploration System, in: RSCTC 2004 (S. Tsumoto, R. Słowinski, H. J. Komorowski, J. W. Grzymała-Busse, Eds.), vol. 3066 of LNCS, Springer, 2004, 592–601.
  • [10] Błaszczyński, J., Słowiński, R., Szeląg, M.: Sequential covering rule induction algorithm for variable consistency rough set approaches, Inf. Sci., 181(5), 2011, 987–1002.
  • [11] Clark, P., Niblett, T.: The CN2 induction algorithm, Mach. Learn., 3(4), 1989, 261–283.
  • [12] Grzymała-Busse, J. W.: LERS – a system for learning from examples based on rough sets, in: Intelligent Decision Support. Handbook of Applications and Advances of the Rough Sets Theory (R. Słowiński, Ed.),Kluwer Academic Publishers, 1992, 3–18.
  • [13] Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I. H.: The WEKA data mining software: an update, SIGKDD Explorations, 11(1), 2009, 10–18.
  • [14] Liu, B., Abbass, H. A., McKay, B.: Classification rule discovery with ant colony optimization, in: IAT 2003, IEEE Computer Society, 2003, ISBN 0-7695-1931-8, 83–88.
  • [15] Michalski, S., Pietrzykowski, J.: I AQ: A program that discovers rules, AAAI-07 AI Video Competition, http://videolectures.net/aaai07_michalski_iaq/, 2007.
  • [16] Moshkov, M., Piliszczuk, M., Zielosko, B.: Partial Covers, Reducts and Decision Rules in Rough Sets –Theory and Applications, vol. 145 of Studies in Computational Intelligence, Springer, Heidelberg, 2008,ISBN 978-3-540-69027-6.
  • [17] Moshkov, M., Zielosko, B.: Combinatorial Machine Learning – A Rough Set Approach, vol. 360 of Studiesin Computational Intelligence, Springer, Heidelberg, 2011, ISBN 978-3-642-20994-9.
  • [18] Nguyen, H. S.: Approximate boolean reasoning: foundations and applications in data mining, in: T. Rough Sets (J. F. Peters, A. Skowron, Eds.), vol. 4100 of LNCS, Springer, 2006, 334–506.
  • [19] Øhrn, A., Komorowski, J., Skowron, A., Synak, P.: The design and implementation of a knowledge discoverytoolkit based on rough sets: The ROSETTA system, in: Rough Sets in Knowledge Discovery 1: Methodology and Applications, vol. 18 of Studies in Fuzziness and Soft Computing, Physica-Verlag, 1998, 376–399.
  • [20] Pawlak, Z., Skowron, A.: Rough sets and Boolean reasoning, Inf. Sci., 177(1), 2007, 41–73.
  • [21] Quinlan, J. R.: C4.5: Programs for Machine Learning, Morgan Kaufmann Publishers Inc., 1993, ISBN1-55860-238-0.
  • [22] Rissanen, J.: Modeling by shortest data description, Automatica, 14(5), 1978, 465–471.
  • [23] Sikora, M.: Decision rule-based data models using TRS and NetTRS – methods and algorithms, T. Rough Sets, 11, 2010, 130–160.
  • [24] Skowron, A., Rauszer, C.: The discernibility matrices and functions in information systems, in: Intelligent Decision Support. Handbook of Applications and Advances of the Rough Set Theory (R. Słowinski, Ed.),Kluwer Academic Publishers, Dordrecht, 1992, 331–362.
  • [25] Ślęzak, D.,Wróblewski, J.: Order based genetic algorithms for the search of approximate entropy reducts, in: RSFDGrC 2003 (G. Wang, Q. Liu, Y. Yao, A. Skowron, Eds.), vol. 2639 of LNCS, Springer, 2003, 308–311.
  • [26] Zielosko, B.: Sequential optimization of -decision rules, in: FedCSIS (M. Ganzha, L. A. Maciaszek,M. Paprzycki, Eds.), 2012, 339–346.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-32989dcb-f5d1-492a-b38b-fbcb9341eed8
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