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Date
2023
Abstract
We showcase and demonstrate IDPP, a Pyrus-based tool that offers a collection of pipelines for the analysis of imbalanced datasets. Like Pyrus, IDPP is a web-based, low-code/no-code graphical modelling environment for ML and data analytics applications. On a case study from the medical domain, we solve the challenge of re-using AI/ML models that do not address data with imbalanced class by implementing ML algorithms in Python that do the re-balancing. We then use these algorithms and the original ML models in the IDPP pipelines. With IDPP, our low-code development approach to balance datasets for AI/ML applications can be used by non-coders. It simplifies the data-preprocessing stage of any AI/ML project pipeline, which can potentially improve the performance of the models. The tool demo will showcase the low-code implementation and no-code reuse and repurposing of AI-based systems through end-to end Pyrus pipelines.
Supervisor
Description
Publisher
Association for Computing Machinery
Citation
Engineering of Computer-Based Systems: 8th International Conference, ECBS 2023, Västerås, Sweden, October 16–18, 2023, ProceedingsOct 2023, pp. 60-69
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Singh_2023_IDPP.pdf
Adobe PDF, 501.68 KB