Thuật toán làm mát giả lập dựa trên trục xoay để xác định các phân tách chéo cho việc tạo ra cây quyết định

Avis D (2000) A revised implementation of the reverse search vertex enumeration algorithm. In: Kalai G, Ziegler GM (eds) Polytopes—combinatorics and Computation. Birkhäuser Basel, Basel, pp 177–198. https://doi.org/10.1007/978-3-0348-8438-9_9

Avis D, Fukuda K (1992) A pivoting algorithm for convex hulls and vertex enumeration of arrangements and polyhedra. Discret Comput Geom 8(3):295–313. https://doi.org/10.1007/BF02293050

Bertsimas D, Dunn J (2017) Optimal classification trees. Mach Learn 106(7):1039–1082. https://doi.org/10.1007/s10994-017-5633-9

Blanquero R, Carrizosa E, Molero-Río C, Romero Morales D (2020) Sparsity in optimal randomized classification trees. Eur J Oper Res 284(1):255–272. https://doi.org/10.1016/j.ejor.2019.12.002

Blanquero R, Carrizosa E, Molero-Río C, Romero Morales D (2021) Optimal randomized classification trees. Comp Oper Res 132:105281. https://doi.org/10.1016/j.cor.2021.105281

Bollwein F, Westphal S (2022) Oblique decision tree induction by cross-entropy optimization based on the von Mises–Fisher distribution. Comput Stat. https://doi.org/10.1007/s00180-022-01195-7

Bradley PS, Mangasarian OL (1998) Feature selection via concave minimization and support vector machines. In: Proceedings of the 15th international conference on machine learning, pp 82–90. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, ICML ’98

Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. Chapman and Hall/CRC, London. https://doi.org/10.1201/9781315139470

Cantú-Paz E, Kamath C (2003) Inducing oblique decision trees with evolutionary algorithms. IEEE Trans Evol Comput 7(1):54–68. https://doi.org/10.1109/TEVC.2002.806857

Dantzig G, Orden A, Wolfe P (1955) The generalized simplex method for minimizing a linear form under linear inequality restraints. Pac J Math 5(2):183–195. https://doi.org/10.2140/pjm.1955.5.183

Demšar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30

Dunn JW (2018) Optimal trees for prediction and prescription. PhD thesis, Massachusetts Institute of Technology

Edelsbrunner H (2012) Algorithms in combinatorial geometry, vol 10. Springer, Berlin

Fisher RA (1936) The use of multiple measurements in taxonomic problems. Ann Eugen 7(2):179–188. https://doi.org/10.1111/j.1469-1809.1936.tb02137.x

Friedman M (1937) The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J Am Stat Assoc 32(200):675–701. https://doi.org/10.1080/01621459.1937.10503522

Friedman M (1940) A comparison of alternative tests of significance for the problem of m rankings. Ann Math Stat 11(1):86–92. https://doi.org/10.1214/aoms/1177731944

Gendreau M, Potvin JY et al (2010) Handbook of metaheuristics, vol 2. Springer, Berlin

Grötschel M, Lovász L, Schrijver A (2012) Geometric algorithms and combinatorial optimization, vol 2. Springer, Berlin

Gurobi Optimization, LLC (2022) Gurobi Optimizer Reference Manual. https://www.gurobi.com, Accesed 4 Feb 2022

Heath DG (1993) A geometric framework for machine learning. PhD thesis, Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA

Heath D, Kasif S, Salzberg S (1993) Induction of oblique decision trees. In: Proceedings of the 13th international joint conference on artificial intelligence, pp 1002–1007. Morgan Kaufmann Publishers

Holm S (1979) A simple sequentially rejective multiple test procedure. Scand J Stat 6(2):65–70

López-Chau A, Cervantes J, López-García L, Lamont FG (2013) Fisher’s decision tree. Expert Syst Appl 40(16):6283–6291. https://doi.org/10.1016/j.eswa.2013.05.044

Manwani N, Sastry P (2011) Geometric decision tree. IEEE Trans Syst Man Cybern Part B (Cybern) 42(1):181–192

Metropolis N, Rosenbluth AW, Rosenbluth MN, Teller AH, Teller E (1953) Equation of state calculations by fast computing machines. J Chem Phys 21(6):1087–1092. https://doi.org/10.1063/1.1699114

Murthy SK, Kasif S, Salzberg S, Beigel R (1993) OC1: a randomized algorithm for building oblique decision trees. In: Proceedings of AAAI, pp 322–327. Citeseer

Murthy SK, Kasif S, Salzberg S (1994) A system for induction of oblique decision trees. J Artif Intell Res 2:1–32. https://doi.org/10.1613/jair.63

Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825–2830

Truong AKY (2009) Fast growing and interpretable oblique trees via logistic regression models. PhD thesis, Oxford University, Oxford, United Kingdom

Wickramarachchi D, Robertson B, Reale M, Price C, Brown J (2016) Hhcart: an oblique decision tree. Comput Stat Data Anal 96:12–23. https://doi.org/10.1016/j.csda.2015.11.006

Wickramarachchi D, Robertson B, Reale M, Price C, Brown J (2019) A reflected feature space for cart. Aust N Z J Stat 61(3):380–391. https://doi.org/10.1111/anzs.12275