Abstract
Data sets with imbalanced class distribution pose serious challenges to well-established classifiers. In this work, we propose a stochastic multi-objective genetic programming based on semantics. We tested this approach on imbalanced binary classification data sets, where the proposed approach is able to achieve, in some cases, higher recall, precision and F-measure values on the minority class compared to C4.5, Naive Bayes and Support Vector Machine, without significantly decreasing these values on the majority class.
E. Galván-López—Research conducted during Galván’s stay at TAO, INRIA and LRI, CNRS & U. Paris-Sud, Université Paris-Saclay, France.
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Notes
- 1.
For the abalone data sets, we substituted F, M and I by 1, 2 and 3, respectively.
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Acknowledgements
EGL’s research is funded by an ELEVATE Fellowship, the Irish Research Council’s Career Development Fellowship co-funded by Marie Curie Actions. EGL would like to thank the TAO group at INRIA Saclay France for hosting him during the outgoing phase of the fellowship. LVM thanks the SSSP for hosting her during her research visit at TCD. The authors would like to thank the reviewers for their comments that helped us to improve our work. EGL would also like to thank E. Mezura-Montes, O. Ait Elhara and M. Schoenauer for their earlier involvement in this work.
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Galván-López, E., Vázquez-Mendoza, L., Trujillo, L. (2017). Stochastic Semantic-Based Multi-objective Genetic Programming Optimisation for Classification of Imbalanced Data. In: Pichardo-Lagunas, O., Miranda-Jiménez, S. (eds) Advances in Soft Computing. MICAI 2016. Lecture Notes in Computer Science(), vol 10062. Springer, Cham. https://doi.org/10.1007/978-3-319-62428-0_22
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