IDPP: Imbalanced Datasets Pipelines in Pyrus

Amandeep Singh, Olga Minguett

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationEngineering of Computer-Based Systems - 8th International Conference, ECBS 2023, Proceedings
EditorsJan Kofroň, Tiziana Margaria, Cristina Seceleanu
PublisherSpringer Science and Business Media Deutschland GmbH
Pages60-69
Number of pages10
ISBN (Print)9783031492518
DOIs
Publication statusPublished - 2024
Event8th International Conference on Engineering of Computer-Based Systems, ECBS 2023 - Västerås, Sweden
Duration: 16 Oct 202318 Oct 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14390 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference8th International Conference on Engineering of Computer-Based Systems, ECBS 2023
Country/TerritorySweden
CityVästerås
Period16/10/2318/10/23

Keywords

  • AI/ML-systems
  • data resampling techniques
  • imbalanced medical datasets
  • Low-code
  • Pyrus
  • Responsible AI

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