Uma Introdução ao Meta-aprendizado
Abstract
Despite the popularization of Machine Learning (ML) techniques, there are several decisions that a user must make in order to be successful in his/her ML solution. As an alternative to the trial and error approach, Metalearning allows to extract knowledge from past problems in which ML techniques have been used in order to recommend situations for new problems the best solution. This tutorial presents an introduction to the design of a metalearner system to recommend ML techniques for classification problems.References
Brazdil, P., Giraud-Carrier, C. G., Soares, C., and Vilalta, R. (2009). Metalearning - Applications to Data Mining. Springer, 1 edition.
Dua, D. and Graff, C. (2017). UCI machine learning repository.
Lorena, A. C., Garcia, L. P., Lehmann, J., Souto, M. C., and Ho, T. K. (2019). How complex is your classication problem? a survey on measuring classication complexity. ACM Computing Surveys (CSUR), 52(5):1–34.
Macià, N. and Bernadó-Mansilla, E. (2014). Towards uci+: A mindful repository design. Information Sciences, 261:237–262.
Prudêncio, R. B. C., Soares, C., and Ludermir, T. B. (2011). Uncertainty sampling-based active selection of datasetoids for meta-learning. In 21th International Conference on Articial Neural Networks (ICANN), volume 6792, pages 454–461.
Smith-Miles, K. A. (2008). Cross-disciplinary perspectives on meta-learning for algorithm selection. ACM Computing Surveys, 41(1):6:1 – 6:25.
Soares, C., Petrak, J., and Brazdil, P. (2001). Sampling-based relative landmarks: Systematically test-driving algorithms before choosing. In 10th Portuguese Conference on Articial Intelligence (EPIA), pages 88 – 95.
Vanschoren, J. (2019). Meta-learning. In Automated Machine Learning, pages 35–61. Springer, Cham.
Vanschoren, J., Van Rijn, J. N., Bischl, B., and Torgo, L. (2014). Openml: networked science in machine learning. ACM SIGKDD Explorations Newsletter, 15(2):49–60.
Wolpert, D. H. (2002). The supervised learning no-free-lunch theorems. In Soft computing and industry, pages 25–42. Springer. 5https://lpfgarcia.github.io/mtl/meta_base
Dua, D. and Graff, C. (2017). UCI machine learning repository.
Lorena, A. C., Garcia, L. P., Lehmann, J., Souto, M. C., and Ho, T. K. (2019). How complex is your classication problem? a survey on measuring classication complexity. ACM Computing Surveys (CSUR), 52(5):1–34.
Macià, N. and Bernadó-Mansilla, E. (2014). Towards uci+: A mindful repository design. Information Sciences, 261:237–262.
Prudêncio, R. B. C., Soares, C., and Ludermir, T. B. (2011). Uncertainty sampling-based active selection of datasetoids for meta-learning. In 21th International Conference on Articial Neural Networks (ICANN), volume 6792, pages 454–461.
Smith-Miles, K. A. (2008). Cross-disciplinary perspectives on meta-learning for algorithm selection. ACM Computing Surveys, 41(1):6:1 – 6:25.
Soares, C., Petrak, J., and Brazdil, P. (2001). Sampling-based relative landmarks: Systematically test-driving algorithms before choosing. In 10th Portuguese Conference on Articial Intelligence (EPIA), pages 88 – 95.
Vanschoren, J. (2019). Meta-learning. In Automated Machine Learning, pages 35–61. Springer, Cham.
Vanschoren, J., Van Rijn, J. N., Bischl, B., and Torgo, L. (2014). Openml: networked science in machine learning. ACM SIGKDD Explorations Newsletter, 15(2):49–60.
Wolpert, D. H. (2002). The supervised learning no-free-lunch theorems. In Soft computing and industry, pages 25–42. Springer. 5https://lpfgarcia.github.io/mtl/meta_base
Published
2020-08-19
How to Cite
LORENA, Ana; GARCIA, Luis.
Uma Introdução ao Meta-aprendizado. In: REGIONAL SCHOOL OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING, 1. , 2020, São Paulo.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 43-46.
