On the Automatic Design of Decision-Tree Induction Algorithms
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Referências
Barros, R. C., Basgalupp, M. P., de Carvalho, A. C. P. L. F., and Freitas, A. A. (2012a). A Survey of Evolutionary Algorithms for Decision-Tree Induction. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 42(3):291–312.
Barros, R. C., Basgalupp, M. P., Freitas, A. A., and de Carvalho, A. C. P. L. F. (2014). Evolutionary Design of Decision-Tree Algorithms Tailored to Microarray Gene Expression Data Sets. IEEE Transactions on Evolutionary Computation, in press.
Barros, R. C., Cerri, R., Jaskowiak, P. A., and de Carvalho, A. C. P. L. F. (2011). A Bottom-Up Oblique Decision Tree Induction Algorithm. In 11th International Conference on Intelligent Systems Design and Applications, pages 450–456.
Barros, R. C.,Winck, A. T., Machado, K. S., Basgalupp, M. P., de Carvalho, A. C. P. L. F., Ruiz, D. D., and de Souza, O. S. (2012b). Automatic design of decision-tree induction algorithms tailored to flexible-receptor docking data. BMC Bioinformatics, 13.
Basgalupp, M. P., Barros, R. C., da Silva, T. S., and de Carvalho, A. C. P. L. F. (2013). Software effort prediction: a hyper-heuristic decision-tree based approach. In 28th Annual ACM Symposium on Applied Computing, pages 1109–1116.
Bennett, K. and Mangasarian, O. (1994). Multicategory discrimination via linear programming. Optimization Methods and Software, 2:29–39.
Breiman, L. (2001). Random forests. Machine Learning, 45(1):5–32.
Cowling, P., Kendall, G., and Soubeiga, E. (2001). A hyperheuristic approach to scheduling a sales summit. In Burke, E. and Erben, W., editors, Practice and Theory of Automated Timetabling III, volume 2079 of Lecture Notes in Computer Science, pages 176–190. Springer Berlin Heidelberg.
Kim, B. and Landgrebe, D. (1991). Hierarchical classifier design in high-dimensional numerous class cases. IEEE Transactions on Geoscience and Remote Sensing, 29(4):518–528.
Pappa, G. L., Ochoa, G., Hyde, M. R., Freitas, A. A., Woodward, J., and Swan, J. (2013). Contrasting meta-learning and hyper-heuristic research: the role of evolutionary algorithms. Genetic Programming and Evolvable Machines.
Rokach, L. and Maimon, O. (2005). Top-down induction of decision trees classifiers - a survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 35(4):476 – 487.
Smith-Miles, K. A. (2009). Cross-disciplinary perspectives on meta-learning for algorithm selection. ACM Computing Surveys, 41:6:1–6:25.
Wolpert, D. H. and Macready, W. G. (1997). No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1(1):67–82.
Barros, R. C., Basgalupp, M. P., Freitas, A. A., and de Carvalho, A. C. P. L. F. (2014). Evolutionary Design of Decision-Tree Algorithms Tailored to Microarray Gene Expression Data Sets. IEEE Transactions on Evolutionary Computation, in press.
Barros, R. C., Cerri, R., Jaskowiak, P. A., and de Carvalho, A. C. P. L. F. (2011). A Bottom-Up Oblique Decision Tree Induction Algorithm. In 11th International Conference on Intelligent Systems Design and Applications, pages 450–456.
Barros, R. C.,Winck, A. T., Machado, K. S., Basgalupp, M. P., de Carvalho, A. C. P. L. F., Ruiz, D. D., and de Souza, O. S. (2012b). Automatic design of decision-tree induction algorithms tailored to flexible-receptor docking data. BMC Bioinformatics, 13.
Basgalupp, M. P., Barros, R. C., da Silva, T. S., and de Carvalho, A. C. P. L. F. (2013). Software effort prediction: a hyper-heuristic decision-tree based approach. In 28th Annual ACM Symposium on Applied Computing, pages 1109–1116.
Bennett, K. and Mangasarian, O. (1994). Multicategory discrimination via linear programming. Optimization Methods and Software, 2:29–39.
Breiman, L. (2001). Random forests. Machine Learning, 45(1):5–32.
Cowling, P., Kendall, G., and Soubeiga, E. (2001). A hyperheuristic approach to scheduling a sales summit. In Burke, E. and Erben, W., editors, Practice and Theory of Automated Timetabling III, volume 2079 of Lecture Notes in Computer Science, pages 176–190. Springer Berlin Heidelberg.
Kim, B. and Landgrebe, D. (1991). Hierarchical classifier design in high-dimensional numerous class cases. IEEE Transactions on Geoscience and Remote Sensing, 29(4):518–528.
Pappa, G. L., Ochoa, G., Hyde, M. R., Freitas, A. A., Woodward, J., and Swan, J. (2013). Contrasting meta-learning and hyper-heuristic research: the role of evolutionary algorithms. Genetic Programming and Evolvable Machines.
Rokach, L. and Maimon, O. (2005). Top-down induction of decision trees classifiers - a survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 35(4):476 – 487.
Smith-Miles, K. A. (2009). Cross-disciplinary perspectives on meta-learning for algorithm selection. ACM Computing Surveys, 41:6:1–6:25.
Wolpert, D. H. and Macready, W. G. (1997). No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1(1):67–82.
Publicado
28/07/2014
Como Citar
BARROS, Rodrigo; DE CARVALHO, André; FREITAS, Alex.
On the Automatic Design of Decision-Tree Induction Algorithms. In: CONCURSO DE TESES E DISSERTAÇÕES (CTD), 27. , 2014, Brasília.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2014
.
p. 19-24.
ISSN 2763-8820.
