Abstract
The efficacy of active learning in genetic programming (AL-GP) for image processing tasks was explored using two new population-based machine learning systems, decision tree genetic programming and SEE-Segment. Active learning was shown to improve the rate and consistency at which good models are found while reducing the required number of training samples to achieve good solutions in both ML systems. The importance of diversity in ensembles for AL-GP was revealed by varying the definition for diversity when performing active learning with SEE-Segment. It was also demonstrated how AL-GP was deployed in a research setting to help automate and accelerate progress by guiding labelling of training samples (human cells) to inform the development of classification models which were then used to automatically classify cells in video frames.
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Computer support by MSU’s iCER high-performance computing centre is gratefully acknowledged.
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Haut, N., Banzhaf, W., Punch, B., Colbry, D. (2024). Accelerating Image Analysis Research with Active Learning Techniques in Genetic Programming. In: Winkler, S., Trujillo, L., Ofria, C., Hu, T. (eds) Genetic Programming Theory and Practice XX. Genetic and Evolutionary Computation. Springer, Singapore. https://doi.org/10.1007/978-981-99-8413-8_3
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DOI: https://doi.org/10.1007/978-981-99-8413-8_3
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