Czasopismo
2013
|
Vol. 127, nr 1-4
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151--160
Tytuł artykułu
Autorzy
Wybrane pełne teksty z tego czasopisma
Warianty tytułu
Języki publikacji
Abstrakty
Based on dynamic programming approach we design algorithms for sequential optimization of exact and approximate decision rules relative to the length and coverage [3, 4]. In this paper, we use optimal rules to construct classifiers, and study two questions: (i) which rules are better from the point of view of classification – exact or approximate; and (ii) which order of optimization gives better results of classifier work: length, length+coverage, coverage, or coverage+length. Experimental results show that, on average, classifiers based on exact rules are better than classifiers based on approximate rules, and sequential optimization (length+coverage or coverage+length) is better than the ordinary optimization (length or coverage).
Słowa kluczowe
Czasopismo
Rocznik
Tom
Strony
151--160
Opis fizyczny
Bibliogr. 17 poz., tab.
Twórcy
autor
autor
- Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia, beata.zielosko@kaust.edu.sa
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 (F. V. C. Ficarra, Ed.), Blue Herons, Bergamo, Italy, 2011, 29-39.
- [2] Alsolami, F., Chikalov, I., Moshkov, M., Zielosko, B.: Optimization of inhibitory decision rules relative to length and coverage, in: RSKT 2012 (T. Li, H. S. Nguyen, G. Wang, J. W. Grzymała-Busse, R. Janicki, A. E. Hassanien, H. Yu, Eds.), vol. 7414 of LNCS, Springer, 2012, 149-154.
- [3] Amin, T., Chikalov, I., Moshkov, M., Zielosko, B.: Dynamic programming approachfor 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.
- [4] Amin, T., Chikalov, I., Moshkov, M., Zielosko, B.: Dynamic programming approach to optimization of approximate decision rules, Inf. Sci., 221, 2013, 403-418.
- [5] Ang, J., Tan, K., Mamun, A.: An evolutionary memetic algorithm for rule extraction, Expert Systems with Applications, 37(2), 2010, 1302-1315.
- [6] Asuncion, A., Newman, D. J.: UCI Machine Learning Repository, http: //www. ics. uci. edu/~mlearn/, 2007.
- [7] Blaszczynski, J., Slowinski, R., Szelag, M.: Sequential covering rule induction algorithm for variable consistency rough set approaches, Inf. Sci., 181(5), 2011, 987-1002.
- [8] Boryczka, U., Kozak, J.: New algorithms for generation decision trees - Ant-Miner and its modifications, in: Foundations of Computational Intelligence (6) (A. Abraham, A. E. Hassanien, A. C. P. de Leon Ferreira de Carvalho, V Snasel, Eds.), vol. 206 of Studies in Computational Intelligence, Springer, 2009, 229-262.
- [9] Dembczynski, K., Kotlowski, W., Slowinski, R.: ENDER: a statistical framework for boosting decision rules, Data Min. Knowl. Discov., 21(1), 2010, 52-90.
- [10] Lavrac, N., Furnkranz, J., Gamberger, D.: Explicit feature construction and manipulation for covering rule learning algorithms, in: Advances in Machine Learning I (J. Koronacki, Z. W. Ras, S. T. Wierzchon J. Kacprzyk, Eds.), vol. 262, Springer, 2010, 121-146.
- [11] 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.
- [12] 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.
- [13] Moshkov, M., Zielosko, B.: Combinatorial Machine Learning - A Rough Set Approach, vol. 360 of Studies in Computational Intelligence, Springer, Heidelberg, 2011, ISBN 978-3-642-20994-9.
- [14] Nguyen, H. S.: Approximate boolean reasoning: foundations and applications in data mining, in: Transactions on Rough Sets (J. F. Peters, A. Skowron, Eds.), vol. 4100 of LNCS, Springer, 2006, 334-506.
- [15] Pawlak, Z., Skowron, A.: Rough sets and Boolean reasoning, Inf. Sci., 177(1), 2007, 41-73.
- [16] 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. Slowinski, Ed.), Kluwer Academic Publishers, Dordrecht, 1992, 331-362.
- [17] Slezak, D., Wroblewski, J.: Order based genetic algorithms forthe 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.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.baztech-aef066af-cb1d-4e33-82e9-f07b43653f62