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Evaluation of Agreement-Related E-mail Classification Models with Unbalanced Classes

Author

Listed:
  • Marcin Hernes
  • Artur Rot
  • Ewa Walaszczyk
  • Janusz Tyburcy
  • Abigail Hanczyk

Abstract

Purpose: The aim of the research is to evaluate the effectiveness of classification models of agreement-related emails with imbalanced classes, which allows for a more comprehensive assessment of their performance under severely imbalanced data and a better understanding of their behaviour in practical applications. Design/Methodology/Approach: The following machine learning classification methods have been used: Complement Naive Bayes, Logistic Regression, Random Forest, and Support Vector Machine. Findings: This research evaluated the effectiveness of classification models for agreement-related emails with imbalanced classes. Random Forest and Support Vector Machine achieve high values for both Accuracy and balanced Accuracy, demonstrating their strong classification performance. Practical Implications: Random Forest and Support Vector Machine can be implemented in intelligent information systems for a mail dispatcher. Correspondence can be automatically routed to the person responsible for handling the inquiry. This speeds up the process and minimises the risk of an inquiry being overlooked or left unanswered. Originality/Value: Despite a large body of research on email classification, there is still a lack of studies focused on specific applications, such as agreement document classification. In particular, it is rare to simultaneously examine different models and compare their performance using multiple metrics within a single real-world problem.

Suggested Citation

  • Marcin Hernes & Artur Rot & Ewa Walaszczyk & Janusz Tyburcy & Abigail Hanczyk, 2026. "Evaluation of Agreement-Related E-mail Classification Models with Unbalanced Classes," European Research Studies Journal, European Research Studies Journal, vol. 0(2), pages 345-356.
    • Handle: RePEc:ers:journl:v:xxix:y:2026:i:2:p:345-356
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    References listed on IDEAS

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    1. Takaya Saito & Marc Rehmsmeier, 2015. "The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-21, March.
    2. Michael Owusu-Adjei & James Ben Hayfron-Acquah & Twum Frimpong & Gaddafi Abdul-Salaam, 2023. "Imbalanced class distribution and performance evaluation metrics: A systematic review of prediction accuracy for determining model performance in healthcare systems," PLOS Digital Health, Public Library of Science, vol. 2(11), pages 1-19, November.
    3. Masao Iwagami & Ryota Inokuchi & Eiryo Kawakami & Tomohide Yamada & Atsushi Goto & Toshiki Kuno & Yohei Hashimoto & Nobuaki Michihata & Tadahiro Goto & Tomohiro Shinozaki & Yu Sun & Yuta Taniguchi & J, 2024. "Comparison of machine-learning and logistic regression models for prediction of 30-day unplanned readmission in electronic health records: A development and validation study," PLOS Digital Health, Public Library of Science, vol. 3(8), pages 1-16, August.
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    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software
    • M15 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - IT Management

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