Abstract

Feature construction (FC) is a crucial step in the machine learning pipeline, as the quality of features can significantly impact the model's performance. This presentation aims to acquaint listeners with feature construction and briefly overview the state-of-the-art FC methods.

The primary focus of the presentation will be an experiment that was conducted using two FC frameworks based on genetic programming (GP) – Evolutionary Forest and M3GP.

Visual Abstract

Slides

Readings

1. Vouk, B., Guid, M., Robnik-Sikonja, M.: Feature construction using explanations of individual predictions. Engineering Applications of Artificial Intelligence 120, 105823 (2023) https://doi.org/10.1016/j.engappai.2023.105823
2. GP (figure): https://www.researchgate.net/figure/Genetic-programming-Tree-based-crossover_fig4_282769665 
3. H. Zhang, A. Zhou and H. Zhang: An Evolutionary Forest for Regression. IEEE Transactions on Evolutionary Computation, vol. 26, no. 4, pp. 735-749, Aug. 2022, https://doi.org/10.1109/TEVC.2021.3136667  
4. H. Zhang, A. Zhou, Q. Chen, B. Xue, and M. Zhang: SR-Forest: A Genetic Programming based Heterogeneous Ensemble Learning Method. IEEE Transactions on Evolutionary Computation, https://doi.org/10.1109/TEVC.2023.3243172  https://github.com/hengzhe-zhang/EvolutionaryForest 
5. J. E. Batista and S. Silva: Comparative study of classifier performance using automatic feature construction by M3GP. 2022 IEEE Congress on Evolutionary Computation (CEC), Padua, Italy, 2022, pp. 1-8, https://doi.org/10.1109/CEC55065.2022.9870343 
6. Muñoz, L., Trujillo, L., & Silva, S. (2015). M3GP - multiclass classification with GP. In Genetic Programming - 18th European Conference, EuroGP 2015, Proceedings (Vol. 9025, pp. 78-91). LNCS Vol. 9025. Springer, Cham.  https://doi.org/10.1007/978-3-319-16501-1_7 https://github.com/jespb/Python-M3GP 

Catering

TBA