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A New Predictive Method for Classification Tasks in Machine Learning: Multi-Class Multi-Label Logistic Model Tree (MMLMT)

Author

Listed:
  • Bita Ghasemkhani

    (Graduate School of Natural and Applied Sciences, Dokuz Eylul University, Izmir 35390, Turkey)

  • Kadriye Filiz Balbal

    (Department of Computer Science, Dokuz Eylul University, Izmir 35390, Turkey)

  • Derya Birant

    (Department of Computer Engineering, Dokuz Eylul University, Izmir 35390, Turkey)

Abstract

This paper introduces a novel classification method for multi-class multi-label datasets, named multi-class multi-label logistic model tree (MMLMT). Our approach supports multi-label learning to predict multiple class labels simultaneously, thereby enhancing the model’s capacity to capture complex relationships within the data. The primary goal is to improve the accuracy of classification tasks involving multiple classes and labels. MMLMT integrates the logistic regression (LR) and decision tree (DT) algorithms, yielding interpretable models with high predictive performance. By combining the strengths of LR and DT, our method offers a flexible and powerful framework for handling multi-class multi-label data. Extensive experiments demonstrated the effectiveness of MMLMT across a range of well-known datasets with an average accuracy of 85.90%. Furthermore, our method achieved an average of 9.87% improvement compared to the results of state-of-the-art studies in the literature. These results highlight MMLMT’s potential as a valuable approach to multi-label learning.

Suggested Citation

  • Bita Ghasemkhani & Kadriye Filiz Balbal & Derya Birant, 2024. "A New Predictive Method for Classification Tasks in Machine Learning: Multi-Class Multi-Label Logistic Model Tree (MMLMT)," Mathematics, MDPI, vol. 12(18), pages 1-27, September.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:18:p:2825-:d:1476454
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    References listed on IDEAS

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    1. Tala Talaei Khoei & Naima Kaabouch, 2023. "Machine Learning: Models, Challenges, and Research Directions," Future Internet, MDPI, vol. 15(10), pages 1-29, October.
    2. Viet-Ha Nhu & Ataollah Shirzadi & Himan Shahabi & Sushant K. Singh & Nadhir Al-Ansari & John J. Clague & Abolfazl Jaafari & Wei Chen & Shaghayegh Miraki & Jie Dou & Chinh Luu & Krzysztof Górski & Binh, 2020. "Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms," IJERPH, MDPI, vol. 17(8), pages 1-30, April.
    3. Ning Li & Masoud Zare & Congke Yi & Rafael Jimenez, 2022. "Stability Risk Assessment of Underground Rock Pillars Using Logistic Model Trees," IJERPH, MDPI, vol. 19(4), pages 1-19, February.
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