Building predictive models via feature synthesis

GECCO 2015 - Proceedings of the 2015 Genetic and Evolutionary Computation Conference

Volumn , Issue , 2015, Pages 983-990

  Arnaldo, Ignacio ^a   O'reilly, Una May ^a   Veeramachaneni, Kalyan ^a  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Feature subset selection; Feature Synthesis; Regression

Indexed keywords

CALCULATIONS; GENETIC ALGORITHMS; GENETIC PROGRAMMING; REGRESSION ANALYSIS;

FEATURE SUBSET SELECTION; FEATURE SYNTHESIS; MULTIPLE REGRESSIONS; PREDICTIVE MODELS; REGRESSION; REGRESSION METHOD; SYMBOLIC REPRESENTATION; VARIABLE SELECTION;

FEATURE EXTRACTION;

EID: 84963657763     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2739480.2754693     Document Type: Conference Paper

References (20)

1. Multiple regression genetic programming in Java. http://flexgp. github.io/gp-learners/, 2014.
2. I. Arnaldo, K. Krawiec, and U.-M. O'Reilly. Multiple regression genetic programming. In Proceedings of the 2014 Conference on Genetic and Evolutionary Computation, GECCO '14, pages 879-886, New York, NY, USA, 2014. ACM.
3. T. Bertin-Mahieux, D. Ellis, B. Whitman, and P. Lamere. The million song dataset. In ISMIR 2011: Proceedings of the 12th International Society for Music Information Retrieval Conference, October 24-28, Miami, Florida, pages 591-596, 2011.
4. V. V. De Melo. Kaizen programming. In Proceedings of the 2014 Conference on Genetic and Evolutionary Computation, GECCO '14, pages 895-902, New York, NY, USA, 2014. ACM.
5. J. H. Friedman, T. Hastie, and R. Tibshirani. Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software, 33(1):1-22, 2 2010.
6. Y. Ganjisaffar. Lasso4j. https://code.google.com/p/lasso4j/, 2014.
7. M. Garcia-Limon, H. J. Escalante, E. Morales, and A. Morales-Reyes. Simultaneous generation of prototypes and features through genetic programming. In Proceedings of the 2014 Conference on Genetic and Evolutionary Computation, GECCO '14, pages 517-524, New York, NY, USA, 2014. ACM.
8. C. Gathercole and P. Ross. Dynamic training subset selection for supervised learning in genetic programming. In Y. Davidor, H.-P. Schwefel, and R. Manner, editors, Parallel Problem Solving from Nature, PPSN III, volume 866 of Lecture Notes in Computer Science, pages 312-321. Springer Berlin Heidelberg, 1994.
9. I. Guyon and A. Elisseeff. An introduction to variable and feature selection. Journal of Machine Learning Research, 3:1157-1182, Mar. 2003.
10. T. Hastie, R. Tibshirani, and J. Friedman. The elements of statistical learning: data mining, inference and prediction. Springer, 2nd edition, 2009.
11. I. Icke and J. Bongard. Improving genetic programming based symbolic regression using deterministic machine learning. In 2013 IEEE Congress on Evolutionary Computation (CEC), pages 1763-1770, June 2013.
12. U. Kamath, J. Lin, and K. De Jong. SAX-EFG: An evolutionary feature generation framework for time series classification. In Proceedings of the 2014 Conference on Genetic and Evolutionary Computation, GECCO '14, pages 533-540, New York, NY, USA, 2014. ACM.
13. K. Krawiec and U.-M. O'Reilly. Behavioral programming: A broader and more detailed take on semantic GP. In Proceedings of the 2014 Conference on Genetic and Evolutionary Computation, GECCO '14, pages 935-942, New York, NY, USA, 2014. ACM.
14. J. Langford, L. Li, and T. Zhang. Sparse online learning via truncated gradient. Journal of Machine Learning Research, 10:777-801, June 2009.
15. Y. Lin and B. Bhanu. Evolutionary feature synthesis for object recognition. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 35(2):156-171, May 2005.
16. MathWorks. Neural network toolbox, 2014.
17. T. McConaghy. FFX: Fast, scalable, deterministic symbolic regression technology. In R. Riolo, E. Vladislavleva, and J. H. Moore, editors, Genetic Programming Theory and Practice IX, Genetic and Evolutionary Computation, pages 235-260. Springer New York, 2011.
18. R. Tibshirani. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society, Series B, 58:267-288, 1994.
19. A. Tsanas and A. Xifara. Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools. Energy and Buildings, 49(0):560-567, 2012.
20. F. Xue, R. Subbu, and P. Bonissone. Locally weighted fusion of multiple predictive models. In International Joint Conference on Neural Networks, 2006. IJCNN '06., pages 2137-2143, 2006.