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    Area of Science:

    • Machine Learning
    • Data Science
    • Computer Science

    Background:

    • Imbalanced classification is common in real-world applications.
    • Accuracy Rate (AR) is often inappropriate for imbalanced data.
    • Non-decomposable measures like AUC and Fβ are more suitable.

    Purpose of the Study:

    • Develop a new minimax probability machine for the Fβ measure (MPMF).
    • Address the limitations of existing minimax probability machines in imbalanced classification.
    • Extend the MPMF model for other non-decomposable performance measures.

    Main Methods:

    • Derived an equivalent form of the MPMF model for effective solving.
    • Employed an alternating descent method to learn a linear classifier.
    • Utilized the kernel trick to develop a nonlinear MPMF model.

    Main Results:

    • The proposed MPMF model effectively handles imbalanced classification tasks.
    • Both linear and nonlinear MPMF models were developed.
    • Experiments on benchmark datasets demonstrated the model's effectiveness.

    Conclusions:

    • MPMF is a suitable criterion for imbalanced classification tasks.
    • The model offers an effective approach for optimizing non-decomposable performance measures.
    • The MPMF framework shows promise for broader applications in imbalanced learning.