Genetic Programming for Interpretable and Explainable Machine Learning

Created by W.Langdon from gp-bibliography.bib Revision:1.8010

@InProceedings{Hu:2022:GPTP,

• author = "Ting Hu",
• title = "Genetic Programming for Interpretable and Explainable Machine Learning",
• booktitle = "Genetic Programming Theory and Practice XIX",
• year = "2022",
• editor = "Leonardo Trujillo and Stephan M. Winkler and Sara Silva and Wolfgang Banzhaf",
• series = "Genetic and Evolutionary Computation",
• pages = "81--90",
• address = "Ann Arbor, USA",
• month = jun # " 2-4",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, XAI",
• isbn13 = "978-981-19-8459-4",
• URL = "https://drive.google.com/file/d/1--GmAFCPNfOHtvOWQr5p3Fzabn5CL5Nj/view?pli=1",
• DOI = "doi:10.1007/978-981-19-8460-0_4",
• size = "ten pages",
• abstract = "Increasing demand for human understanding of machine decision-making is deemed crucial for machine learning (ML) methodology development and further applications. It has inspired the emerging research field of interpretable and explainable ML/AI. Techniques have been developed to either provide additional explanations to a trained ML model or learn innately compact and understandable models. Genetic programming (GP), as a powerful learning instrument, holds great potential in interpretable and explainable learning. In this chapter, we first discuss concepts and popular methods in interpretable and explainable ML, and review research using GP for interpretability and explainability. We then introduce our previously proposed GP-based framework for interpretable and explainable learning applied to bioinformatics.",
• notes = "Part of \cite{Banzhaf:2022:GPTP} published after the workshop in 2023",

}

Genetic Programming entries for Ting Hu

Citations