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Handling the Imbalanced Problem in Agri-Food Data Analysis.

Adeyemi O Adegbenjo1,2, Michael O Ngadi1

  • 1Department of Bioresource Engineering, McGill University, 21111 Lakeshore Road, Ste-Anne-de-Bellevue, Montreal, QC H9X 3V9, Canada.

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Imbalanced data in food processing leads to inaccurate predictive models. This study proposes advanced artificial intelligence methods to improve classification accuracy and model adoption in agri-food applications.

Keywords:
food processingimbalanced datainnovation adoptabilitymachine learning algorithms

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Area of Science:

  • Agricultural Science
  • Food Science
  • Computer Science

Background:

  • Imbalanced data is a significant challenge in machine learning, particularly in food processing.
  • Existing algorithms like SVMs struggle with rare cases, leading to misclassification and unreliable predictive models.
  • This limits the adoption of new technologies in the agri-food industry.

Purpose of the Study:

  • To highlight the prevalence and impact of imbalanced data in agri-food applications.
  • To propose advanced artificial intelligence (AI) techniques for effectively handling imbalanced data.
  • To evaluate suitable metrics for imbalanced data analysis in this domain.

Main Methods:

  • Data resampling techniques
  • One-class learning approaches
  • Ensemble methods
  • Feature selection strategies
  • Deep learning models
  • Evaluation of specialized metrics for imbalanced datasets

Main Results:

  • Demonstration of the imbalanced data problem in agri-food contexts.
  • Proposal of AI-driven solutions including resampling, one-class learning, ensemble methods, feature selection, and deep learning.
  • Evaluation of appropriate metrics for assessing model performance with imbalanced data.

Conclusions:

  • Accurate analysis of imbalanced data is crucial for robust model development in food processing.
  • Implementing advanced AI techniques and suitable metrics enhances model accuracy and reliability.
  • Improved model performance will increase the acceptance and adoption of innovations in the agri-food sector.