nach oben

Erschienen in:

2020 | OriginalPaper | Buchkapitel

Decision Trees with at Most 19 Vertices for Knowledge Representation

verfasst von : Mohammad Azad

Erschienen in: Transactions on Rough Sets XXII

Verlag: Springer Berlin Heidelberg

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

We study decision trees as a means of representation of knowledge. To this end, we design two techniques for the creation of CART (Classification and Regression Tree)-like decision trees that are based on bi-objective optimization algorithms. We investigate three parameters of the decision trees constructed by these techniques: number of vertices, global misclassification rate, and local misclassification rate.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Nächstes Kapitel ||-ROSETTA

AbouEisha, H., Amin, T., Chikalov, I., Hussain, S., Moshkov, M.: Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining. Intelligent Systems Reference Library, vol. 146. Springer, Heidelberg (2019). https://doi.org/10.1007/978-3-319-91839-6CrossRef

Alsolami, F., Azad, M., Chikalov, I., Moshkov, M.: Decision and Inhibitory Trees and Rules for Decision Tables with Many-Valued Decisions. Intelligent Systems Reference Library, vol. 156. Springer, Heidelberg (2020). https://doi.org/10.1007/978-3-030-12854-8CrossRefMATH

Azad, M.: Decision and inhibitory trees for decision tables with many-valued decisions. Ph.D. thesis, King Abdullah University of Science & Technology, Thuwal, Saudi Arabia (2018). http://hdl.handle.net/10754/628023

Azad, M.: Knowledge representation using decision trees constructed based on binary splits. KSII Trans. Internet Inf. Syst. 14(10), 4007–4024 (2020). https://doi.org/10.3837/tiis.2020.10.005

Azad, M., Chikalov, I., Hussain, S., Moshkov, M.: Restricted multi-pruning of decision trees. In: 13th International FLINS Conference on Data Science and Knowledge Engineering for Sensing Decision Support, FLINS, pp. 371–378 (2018). https://doi.org/10.1142/9789813273238_0049

Azad, M., Chikalov, I., Hussain, S., Moshkov, M.: Multi-pruning of decision trees for knowledge representation and classification. In: 3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015, Kuala Lumpur, Malaysia, 3–6 November 2015, pp. 604–608. IEEE (2015). https://doi.org/10.1109/ACPR.2015.7486574

Azad, M., Chikalov, I., Moshkov, M.: Decision trees for knowledge representation. In: Ropiak, K., Polkowski, L., Artiemjew, P. (eds.) 28th International Workshop on Concurrency, Specification and Programming, CS&P 2019, Olsztyn, Poland, 24–26 September 2019. CEUR Workshop Proceedings, vol. 2571. CEUR-WS.org (2019). http://ceur-ws.org/Vol-2571/CSP2019_paper_1.pdf

Azad, M., Chikalov, I., Moshkovc, M.: Representation of knowledge by decision trees for decision tables with multiple decisions. Proc. Comput. Sci. 176, 653–659 (2020). https://doi.org/10.1016/j.procs.2020.09.037

Boryczka, U., Kozak, J.: New algorithms for generation decision trees - ant-miner and its modifications. In: Abraham, A., Hassanien, A.E., de Leon Ferreira de Carvalho, A.C.P., Snásel, V. (eds.) Foundations of Computational Intelligence - Volume 6: Data Mining, vol. 206, pp. 229–262. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-01091-0_11CrossRef

10.

Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. Wadsworth and Brooks, Monterey (1984)MATH

11.

Breitbart, Y., Reiter, A.: A branch-and-bound algorithm to obtain an optimal evaluation tree for monotonic boolean functions. Acta Inf. 4, 311–319 (1975)MathSciNetCrossRef

12.

Chai, B., Zhuang, X., Zhao, Y., Sklansky, J.: Binary linear decision tree with genetic algorithm. In: 13th International Conference on Pattern Recognition, ICPR 1996, Vienna, Austria, 25–19 August 1996, vol. 4, pp. 530–534. IEEE (1996)

13.

Chikalov, I., Hussain, S., Moshkov, M.: Totally optimal decision trees for Boolean functions. Discrete Appl. Math. 215, 1–13 (2016)MathSciNetCrossRef

14.

Delimata, P., Moshkov, M., Skowron, A., Suraj, Z.: Inhibitory Rules in Data Analysis: A Rough Set Approach, Studies in Computational Intelligence, vol. 163. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-540-85638-2CrossRefMATH

15.

Dua, D., Graff, C.: UCI machine learning repository. University of California, Irvine, School of Information and Computer Sciences (2017). http://archive.ics.uci.edu/ml

16.

Garey, M.R.: Optimal binary identification procedures. SIAM J. Appl. Math. 23, 173–186 (1972)MathSciNetCrossRef

17.

Heath, D.G., Kasif, S., Salzberg, S.: Induction of oblique decision trees. In: Bajcsy, R. (ed.) 13th International Joint Conference on Artificial Intelligence, IJCAI 1993, Chambéry, France, 28 August–3 September 1993, pp. 1002–1007. Morgan Kaufmann (1993)

18.

Hyafil, L., Rivest, R.L.: Constructing optimal binary decision trees is NP-complete. Inf. Process. Lett. 5(1), 15–17 (1976)MathSciNetCrossRef

19.

Martelli, A., Montanari, U.: Optimizing decision trees through heuristically guided search. Commun. ACM 21(12), 1025–1039 (1978)MathSciNetCrossRef

20.

Moshkov, M.J.: Time complexity of decision trees. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets III. LNCS, vol. 3400, pp. 244–459. Springer, Heidelberg (2005). https://doi.org/10.1007/11427834_12CrossRef

21.

Moshkov, M., Zielosko, B.: Combinatorial Machine Learning - A Rough Set Approach, Studies in Computational Intelligence, vol. 360. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-20995-6CrossRefMATH

22.

Pawlak, Z.: Rough Sets - Theoretical Aspect of Reasoning About Data. Kluwer Academic Publishers, Dordrecht (1991)MATH

23.

Pawlak, Z., Skowron, A.: Rudiments of rough sets. Inf. Sci. 177(1), 3–27 (2007)MathSciNetCrossRef

24.

Riddle, P., Segal, R., Etzioni, O.: Representation design and brute-force induction in a Boeing manufacturing domain. Appl. Artif. Intell. 8, 125–147 (1994)CrossRef

25.

Rokach, L., Maimon, O.: Data Mining with Decision Trees: Theory and Applications. World Scientific Publishing, River Edge (2008)MATH

26.

Schumacher, H., Sevcik, K.C.: The synthetic approach to decision table conversion. Commun. ACM 19(6), 343–351 (1976)CrossRef

27.

Skowron, A., Rauszer, C.: The discernibility matrices and functions in information systems. In: Słowinski, R. (ed.) Intelligent Decision Support. Handbook of Applications and Advances of the Rough Set Theory, pp. 331–362. Kluwer Academic Publishers, Dordrecht (1992)CrossRef

Titel: Decision Trees with at Most 19 Vertices for Knowledge Representation
verfasst von: Mohammad Azad
Verlag: Springer Berlin Heidelberg
Buch: Transactions on Rough Sets XXII
Print ISBN: 978-3-662-62797-6

Electronic ISBN: 978-3-662-62798-3

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-662-62798-3_1

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner