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    This study introduces a cost-sensitive deep neural network to address class imbalance in object detection. The method effectively learns features for both majority and minority classes, outperforming existing techniques.

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    Area of Science:

    • Computer Science
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Class imbalance is a prevalent challenge in real-world object detection and classification.
    • Scarcity of data for minority classes hinders classifier performance in distinguishing between majority and minority groups.

    Purpose of the Study:

    • To propose a cost-sensitive (CoSen) deep neural network for robust feature representation learning in imbalanced datasets.
    • To develop a method that jointly optimizes class-dependent costs and neural network parameters during training.

    Main Methods:

    • A novel cost-sensitive deep neural network (CoSen) approach is introduced.
    • The learning procedure jointly optimizes class-dependent costs and neural network parameters.
    • The method is applicable to both binary and multiclass problems without modification and does not alter original data distribution, reducing computational cost.

    Main Results:

    • The proposed CoSen deep neural network significantly outperforms baseline algorithms on six major image classification datasets.
    • Experimental results demonstrate superior performance compared to popular data sampling techniques and existing CoSen classifiers.
    • The approach effectively learns robust feature representations for both majority and minority classes.

    Conclusions:

    • The proposed cost-sensitive deep neural network effectively addresses class imbalance in object detection and classification tasks.
    • This method offers a computationally efficient and adaptable solution for imbalanced learning problems.
    • The CoSen approach achieves superior performance, highlighting its potential for real-world applications.