Geodesic
Classification based on full decision trees
Žurnal vyčislitelʹnoj matematiki i matematičeskoj fiziki, Tome 52 (2012) no. 4, pp. 750-761 
I. E. Genrihov; E. V. Dyukova. Classification based on full decision trees. Žurnal vyčislitelʹnoj matematiki i matematičeskoj fiziki, Tome 52 (2012) no. 4, pp. 750-761. http://geodesic.mathdoc.fr/item/ZVMMF_2012_52_4_a13/
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     title = {Classification based on full decision trees},
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Voir la notice de l'article provenant de la source Math-Net.Ru

The ideas underlying a series of the authors’ studies dealing with the design of classification algorithms based on full decision trees are further developed. It is shown that the decision tree construction under consideration takes into account all the features satisfying a branching criterion. Full decision trees with an entropy branching criterion are studied as applied to precedent-based pattern recognition problems with real-valued data. Recognition procedures are constructed for solving problems with incomplete data (gaps in the feature descriptions of the objects) in the case when the learning objects are nonuniformly distributed over the classes. The authors’ basic results previously obtained in this area are overviewed.

[1] Zhuravlëv Yu. I., “Ob algebraicheskom podkhode k resheniyu zadach raspoznavaniya i klassifikatsii”, Probl. kibernetiki, 33, Nauka, M., 1978, 5–68

[2] Djukova E. V., Peskov N. V., “A classification algorithm based on the complete decision tree”, J. Pattern Recognition and Image Analysis, 17:3 (2007), 363–367 | DOI

[3] Donskoi V. I., Bashta A. I., Diskretnye modeli prinyatiya reshenii pri nepolnoi informatsii, Tavriya, Simferopol, 1992, 33–74

[4] Genrikhov I. E., “Postroenie polnogo reshayuschego dereva na baze algoritma S4.5”, Coobsch. prikl. matem., VTs RAN, Moskva, 2009 | MR

[5] Genrikhov I. E., Dyukova E. V., “Usovershenstvovanie algoritma S4.5 na osnove ispolzovaniya polnykh reshayuschikh derevev”, Matematicheskie metody raspoznavaniya obrazov, Dokl. 14-i Vseros. konf., MAKS Press, Moskva, 2009, 104–107 | MR

[6] Quinlan J. R., C4.5: Programs for Machine Learning, Morgan Kaufmann, San Mateo, CA, 1993

[7] Genrikhov I. E., Dyukova E. V., “Postroenie i issledovanie raspoznayuschikh protsedur na osnove polnykh reshayuschikh derevev”, Intellektualizatsiya obrabotki informatsii, Dokl. 8-i mezhdunar. konf., MAKS Press, Moskva, 2010, 117–120

[8] Genrikhov I. E., “Synthesis and analysis of recognizing procedures on the basis of full decision trees”, J. Pattern Recognition and Image Analysis, 21:1 (2011), 45–51 | DOI

[9] Quinjan J. R., See5/C5.0 DEMO. RULEQUEST RESEARCH, , 2008 http://www.rulequest.com/see5-info.html

[10] Zhuravlëv Yu. I., Ryazanov V. V., Senko O. V., Raspoznavanie. Matematicheskie metody. Programmnaya sistema, prakticheskie primeneniya, FAZIS, M., 2006

[11] Peng L., Lei L., “A review of missing data treatment methods”, Internat. Intelligent Information Management Systems and Technologies, 1:3 (2005), 412–419

[12] Dyukova E. V. i dr., “Obrabotka veschestvennoznachnoi informatsii logicheskimi protsedurami raspoznavaniya”, Iskusstv. intellekt, 2004, no. 2, 80–85

[13] Fawcett T., “An introduction to ROC analysis”, Pattern Recognition Letters, 27:8 (2006), 861–874 | DOI | MR