G. H. Rosa
ABSTRACT
Feature selection concerns the task of finding the subset of features that are most relevant to some specific problem in the context of machine learning. During the last years, the problem of feature selection has been modeled as an optimization task, where the idea is to find the subset of features that maximize some fitness function, which can be a given classifier's accuracy or even some measure concerning the samples' separability in the feature space, for instance. In this paper, we introduced Geometric Semantic Genetic Programming (GSGP) in the context of feature selection, and we experimentally showed it can work properly with both conic and non-conic fitness landscapes.
- Fevzi Alimoglu and Ethem Alpaydin. 1996. Methods of combining multiple classifiers based on different representations for pen-based handwritten digit recognition. In Proceedings of the Fifth Turkish Artificial Intelligence and Artificial Neural Networks Symposium (TAINN 96. Citeseer.Google Scholar
- M. Castelli, R. Henriques, and L. Vanneschi. 2015. A geometric semantic genetic programming system for the electoral redistricting problem. Neurocomputing 154(2015), 200--207. Google ScholarDigital Library
- R. A. Davis, A. J. Charlton, S. Oehlschlager, and J. C. Wilson. 2006. Novel feature selection method for genetic programming using metabolomic 1H {NMR} data. Chemometrics and Intelligent Laboratory Systems 81, 1 (2006), 50--59.Google ScholarCross Ref
- Chih-Wei Hsu and Chih-Jen Lin. 2002. A comparison of methods for multiclass support vector machines. IEEE transactions on Neural Networks 13, 2 (2002), 415--425. Google ScholarDigital Library
- J.R. Koza. 1992. Genetic programming: on the programming of computers by means of natural selection. The MIT Press, Cambridge, USA. Google ScholarDigital Library
- J.-Y. Lin, H.-R. Ke, B.-C. Chien, and W.-P. Yang. 2008. Classifier Design with Feature Selection and Feature Extraction Using Layered Genetic Programming. Expert Systems with Applications: An International Journal 34, 2 (Feb. 2008), 1384--1393. Google ScholarDigital Library
- A. Moraglio, K. Krawiec, and C. G. Johnson. 2012. Geometric Semantic Genetic Programming. Springer Berlin Heidelberg, Berlin, Heidelberg, 21--31. Google ScholarDigital Library
- J. P. Papa, A. X. Falcão, V. H. C. Albuquerque, and J. M. R. S. Tavares. 2012. Efficient supervised optimum-path forest classification for large datasets. Pattern Recognition 45, 1 (2012), 512--520. Google ScholarDigital Library
- J. P. Papa, A. X. Falcão, and C. T. N. Suzuki. 2009. Supervised pattern classification based on optimum-path forest. International Journal of Imaging Systems and Technology 19, 2 (2009), 120--131. Google ScholarDigital Library
- Ramirez R. and Puiggros M. 2007. A Genetic Programming Approach to Feature Selection and Classification of Instantaneous Cognitive States. Springer Berlin Heidelberg, Berlin, Heidelberg, 311--319. Google ScholarDigital Library
- D. Rodrigues, X.-S. Yang, A. N. Souza, and J. P. Papa. 2015. Recent Advances in Swarm Intelligence and Evolutionary Computation. Springer International Publishing, Cham, Chapter Binary Flower Pollination Algorithm and Its Application to Feature Selection, 85--100.Google Scholar
- L. Vanneschi, S. Silva, M. Castelli, and L. Manzoni. 2014. Geometric Semantic Genetic Programming for Real Life Applications. Springer New York, New York, NY, 191--209.Google Scholar
- F. Wilcoxon. 1945. Individual Comparisons by Ranking Methods. Biometrics Bulletin 1, 6 (1945), 80--83.Google ScholarCross Ref
- J. Yang and V. G. Honavar. 1998. Feature Subset Selection Using a Genetic Algorithm. IEEE Intelligent Systems 13, 2 (March 1998), 44--49. Google ScholarDigital Library
Index Terms
- Feature selection using geometric semantic genetic programming
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