TY - GEN
T1 - Genetic programming representations for multi-dimensional feature learning in biomedical classification
AU - La Cava, William
AU - Vanneschi, Leonardo
AU - Spector, Lee
AU - Moore, Jason
AU - Silva, Sara
N1 - La Cava, W., Vanneschi, L., Spector, L., Moore, J., & Silva, S. (2017). Genetic programming representations for multi-dimensional feature learning in biomedical classification. In Applications of Evolutionary Computation - 20th European Conference, EvoApplications 2017, Proceedings (Vol. 10199 LNCS, pp. 158-173). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10199 LNCS). Springer-Verlag. https://doi.org/10.1007/978-3-319-55849-3_11
PY - 2017
Y1 - 2017
N2 - We present a new classification method that uses genetic programming (GP) to evolve feature transformations for a deterministic, distanced-based classifier. This method, called M4GP, differs from common approaches to classifier representation in GP in that it does not enforce arbitrary decision boundaries and it allows individuals to produce multiple outputs via a stack-based GP system. In comparison to typical methods of classification, M4GP can be advantageous in its ability to produce readable models. We conduct a comprehensive study of M4GP, first in comparison to other GP classifiers, and then in comparison to six common machine learning classifiers. We conduct full hyper-parameter optimization for all of the methods on a suite of 16 biomedical data sets, ranging in size and difficulty. The results indicate that M4GP outperforms other GP methods for classification. M4GP performs competitively with other machine learning methods in terms of the accuracy of the produced models for most problems. M4GP also exhibits the ability to detect epistatic interactions better than the other methods.
AB - We present a new classification method that uses genetic programming (GP) to evolve feature transformations for a deterministic, distanced-based classifier. This method, called M4GP, differs from common approaches to classifier representation in GP in that it does not enforce arbitrary decision boundaries and it allows individuals to produce multiple outputs via a stack-based GP system. In comparison to typical methods of classification, M4GP can be advantageous in its ability to produce readable models. We conduct a comprehensive study of M4GP, first in comparison to other GP classifiers, and then in comparison to six common machine learning classifiers. We conduct full hyper-parameter optimization for all of the methods on a suite of 16 biomedical data sets, ranging in size and difficulty. The results indicate that M4GP outperforms other GP methods for classification. M4GP performs competitively with other machine learning methods in terms of the accuracy of the produced models for most problems. M4GP also exhibits the ability to detect epistatic interactions better than the other methods.
KW - Classification
KW - Feature learning
KW - Genetic programming
UR - http://www.scopus.com/inward/record.url?scp=85017541777&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-55849-3_11
DO - 10.1007/978-3-319-55849-3_11
M3 - Conference contribution
AN - SCOPUS:85017541777
SN - 9783319558486
VL - 10199 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 158
EP - 173
BT - Applications of Evolutionary Computation - 20th European Conference, EvoApplications 2017, Proceedings
PB - Springer-Verlag
T2 - 20th European Conference on the Applications of Evolutionary Computation, EvoApplications 2017
Y2 - 19 April 2017 through 21 April 2017
ER -