Por Sara Silva (LASIGE/FCUL).

Among its many competences, genetic programming (GP) can also be regarded as a feature construction method. M3GP is a GP variant, originally developed for performing multiclass classification, that recently proved to be also a powerful method for evolving hyper-features from the original data, for both classification and regression, and for a variety of machine learning methods. After a short tutorial on GP, this talk will address how the evolved hyper-features can improve the robustness and readability of data models, with examples from remote sensing applications.

Short bio: Sara Silva is Principal Investigator at LASIGE, FCUL. Her research interests are in machine learning with a strong emphasis in genetic programming, where she has contributed with several new methods and applied them in interdisciplinary projects from remote sensing to biomedicine, among others. Author of around 100 peer-reviewed publications, she has received more than 10 nominations and awards for best paper and best researcher. In 2018 she received the EvoStar Award for Outstanding Contribution to Evolutionary Computation in Europe. In 2023 she is the General Chair of the largest conference in evolutionary computation, GECCO, and co-author of the Springer book Lectures on Intelligent Systems. She is the creator and developer of GPLAB - A Genetic Programming Toolbox for MATLAB.

Transmissão via Zoom.