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    This study introduces a new active learning algorithm using Fisher information ratio for efficient data labeling. It achieves competitive classifier performance without assuming test data distribution.

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    Area of Science:

    • Machine Learning
    • Data Science
    • Statistics

    Background:

    • Data labeling is costly and time-consuming.
    • Active learning minimizes training data while maximizing classifier performance.
    • Information-theoretic measures, particularly Fisher information (FI), are popular for query selection due to diversity and computational efficiency.

    Purpose of the Study:

    • To develop a practical algorithm for query distribution in active learning using the Fisher information ratio.
    • To address limitations of previous FI-based methods by not assuming test distribution.

    Main Methods:

    • A novel algorithm based on the Fisher information ratio is proposed.
    • The algorithm obtains query distribution for a general active learning framework.
    • No assumptions are made regarding the test data distribution.

    Main Results:

    • The proposed algorithm demonstrates competitive performance on synthetic and real-world datasets.
    • Empirical results validate the effectiveness of the Fisher information ratio approach.

    Conclusions:

    • The developed algorithm offers an efficient and effective method for query selection in active learning.
    • This approach advances active learning by relaxing assumptions on test distributions, enhancing practical applicability.