Academic Failure at University and Data Processing Methods Based on Decision Trees and Neural Networks: Research Methodology
For a long time, academic failure among university students sparked heated controversy. Many educational psychologists try to figure it out and then explain it. Statisticians have tried to predict it. Our research (article) aims to classify students into several categories, as well as to use the decision tree and artificial neural networks to classify first-year students and identify variables that may explain the problem.
https://fr.wikipedia.org/wiki/Arbre_de_d%C3%A9cision#cite_ref-2.
https://fr.wikipedia.org/wiki/Arbre_de_d%C3%A9cision_(apprentissage)#cite_note-Quinlan86-4.
S. Madeh Piryonesi et Tamer E. El-Diraby, « Data Analytics in Asset Management: Cost-Effective Prediction of the Pavement Condition Index », Journal of Infrastructure Systems, vol. 26, no 1, mars 2020, p. 04019036 (ISSN 1076-0342 et 1943-555X,
DOI 10.1061/(ASCE)IS.1943-555X.0000512.
Xindong Wu, Vipin Kumar, J. Ross Quinlan et Joydeep Ghosh, « Top 10 algorithms in data mining », Knowledge and Information Systems, vol. 14, no 1, janvier 2008, p. 1–37 (ISSN 0219-1377 et 0219-3116, DOI 10.1007/s10115-007-0114-2.
Lior Rokach, Data mining with decision trees : theory and applications, Hackensack (NJ), World Scientific Pub Co Inc, 2008, 244 p. (ISBN 978-981-27-7171-1, notice BnF no FRBNF41351943).
Leo Breiman, Classification and regression trees, Monterey, CA, Wadsworth & Brooks/Cole Advanced Books & Software, 1984, 368 p. (ISBN 978-0-412-04841-8).
William James (1890). The Principles of Psychology. Henry Holt and Company, New York, USA.
W.S. McCulloh, et W.Pitts."A logical calculus of the ideas immanent in nervous activity".the bullettin of mathematical biophysics,,vol.,no 4, p. 115-133. 1943
D. 0. HEBB. (1949). The Organization of Behavior, A NEUROPSYCHOLOGICAL THEORY. McGill University. New York JOHN WILEY if SONS, Inc. London CHAPMAN i HALL, Limited.
In 1951 Marvin Minsky teamed with Dean Edmonds build the first artificial neural network that simulated a rat finding its way through a maze.
F. Rosenblatt (1958), The perceptron: a probabilistic model for information storage and organization in the brain, - repris dans J.A. Anderson & E. Rosenfeld (1988), Neurocomputing. Foundations of Research, MIT Press
B. Widrow, (1960). Adaptive sampled-data systems, IFAC Moscow Congress Record, Butterworth Publications, London.
Minsky, M., & Papert, S. (1969). Perceptrons : An Introduction to Computational Geometry, MIT Press, 268 p.
Grossberg, S.: Adaptive pattern classification and universal recoding. II. Feedback, expectation, olfaction, illusions. Biol. Cybern. 23, 187–202 (1976).
Kohonen, T.: Associative memory—a system-theoretical approach. Berlin, Heidelberg, New York: Springer 1977.
Anderson, J.A.: A simple neural network generating an interactive memory. Math. Biosci. 14, 197–220 (1972)
J J Hopfield, (1982). Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences Apr 1982, 79 (8) 2554-2558; DOI: 10.1073/pnas.79.8.2554
Rumelhart, D. E., Durbin, R., Golden, R., and Chauvin, Y. (1995). Ûackpropagation: The basic theory. In Y. Chauvin and D. E. Rumelhart (eds.),Backpropagation:Theory, Architectures, and Applications. Ðillsdale: NJ. Lawrence Erlbaum, 1-34. Chapter 1.
Wang, L.X. et Mendel, J.M. (1992). Adaptive m1mmum prediction error deconvolution and source wavelet estimation using Hopfield neural networks. Geophysics, 57, 670-679.
Murat, M.E. and Rudman, A.J. " (1992). Automated first arrival picking: a neural network approach. Geophysical Prospecting " 40,587-604, .
Röth, G., Tarantola, A. (1994). "Neural networks and inversion of seismic data". journal of geophysical research, VOL. 99, NO. B4, PAGES 6753-6768, APRIL 10,.
Y. Zhang, K. V. Paulson " (1997a). Magnetotelluric inversion using regularized Hopfield neural networks" Geophysical Prospecting, 45, 725–743,
Y. Zhang, K. V. Paulson " (1997b). Enhancement of Signal-to-noise Ratio in Natural-source Transient Magnetotelluric Data with Wavelet Transform" appl. geophys. 149 - 405–419,.
P. J. Drew, J. R. T. Monson, (2000). "Artificial neural networks".127(1),3–11.
http://informatique.coursgratuits.net/methodes-numeriques/reseaux-de-neurones-formels.php.
http://www.grappa.univ-lille3.fr/polys/apprentissage/sortie005.html
Antoine Cornuéjols, Laurent Miclet, Yves Kodratoff, Apprentissage Artificiel : Concepts et algorithmes, Eyrolles, 2002 (ISBN 2-212-11020-0)
Christopher M. Bishop, Pattern Recognition And Machine Learning, Springer, 2006
Guillaume Cleuziou, Une méthode de classification non-supervisée pour l’apprentissage de règles et la recherche d’information, 10 juillet 2006.
F. Rosenblatt (1958), The perceptron: a probabilistic model for information storage and organization in the brain, - repris dans J.A. Anderson & E. Rosenfeld (1988), Neurocomputing. Foundations of Research, MIT Press.
D. 0. HEBB. (1949). The Organization of Behavior, A neuropsychological theory. McGill University. New York John Wiley if SONS, Inc. London CHAPMAN i HALL, Limited.
Claude Touzet. Les réseaux de neurones artificiels, introduction au connexionnisme : cours, exercices et travaux pratiques. EC2, 1992, Collection de l’EERIE, N. Giambiasi. hal-01338010.
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