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Collaborative Learning of Label Semantics and Deep Label-Specific Features for Multi-Label Classification.

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    This study introduces CLIF, a novel deep neural network for multi-label classification. CLIF collaboratively learns label semantics and label-specific features, improving classification accuracy by enabling mutual guidance between these components.

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    Area of Science:

    • Machine Learning
    • Computer Science
    • Artificial Intelligence

    Background:

    • Multi-label classification utilizes label-specific features for enhanced performance.
    • Existing methods often treat label semantic relations as fixed, limiting feature learning.
    • A need exists for adaptive methods that integrate label semantics and features.

    Purpose of the Study:

    • To propose a collaborative learning framework for multi-label classification.
    • To develop a deep neural network (DNN) approach named CLIF.
    • To jointly learn label semantics and label-specific features.

    Main Methods:

    • CLIF integrates a graph autoencoder for label semantic encoding.
    • A feature-disentangling module extracts label-specific features.
    • The DNN approach enables mutual guidance between label semantics and features.

    Main Results:

    • CLIF effectively guides label-specific feature mining using learned label semantics.
    • Learned label semantics benefit from the propagation of label-specific properties.
    • Experiments on 14 datasets demonstrate superior performance over existing algorithms.

    Conclusions:

    • CLIF offers a synergistic approach to multi-label classification.
    • The collaborative learning strategy enhances both label semantics and feature extraction.
    • This method advances the state-of-the-art in multi-label classification.