Talks@DI: Leonardo Trujillo, Technical Institute of Tijuanain

Title: RANSAC-GP: Dealing with Outliers in Symbolic Regression with Genetic Programming

Abstract:
Genetic programming (GP) has been shown to be a powerful
tool for automatic modeling and program induction. It is often used to solve di cult symbolic regression tasks, with many examples in realworld domains. However, the robustness of GP-based approaches has not been substantially studied. In particular, the present work deals with the issue of outliers, data in the training set that represent severe errors in the measuring process. In general, a datum is considered an outlier when it sharply deviates from the true behavior of the system of interest.
GP practitioners know that such data points usually bias the search
and produce inaccurate models. Therefore, this work presents a hybrid methodology based on the RAndom SAmpling Consensus (RANSAC) algorithm and GP, which we call RANSAC-GP. RANSAC is an approach to deal with outliers in parameter estimation problems, widely used in computer vision and related  
elds. On the other hand, this work presents the   rst application of RANSAC to symbolic regression with GP, with impressive results. The proposed algorithm is able to deal with extreme amounts of contamination in the training set, evolving highly accurate models even when the amount of outliers reaches 90%.

Short Bio:
Leonardo Trujillo received a degree in Electronic Engineering(2002) and a Masters in Computer Science (2004) from the Technical Institute of Tijuana, in Tijuana México. He also received a doctorate in Computer Science from the CICESE research center, in Ensenada Mexico (2008), developing Genetic Programming (GP) applications for Computer Vision problems, focusing on feature extraction and image description.
He is currently professor at the Technical Institute of Tijuanain, in México, where he is currently President of the Masters Program, head of the Cybernetics research group, is the head researcher of the TREE-LAB tesearch lab and member of the National System of Researchers (level I). His primary research interests are evolutionary computation, genetic programming, pattern recognition and machine learning.