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An Interpretable Adaptive Multiscale Attention Deep Neural Network for Tabular Data.

Vincenzo Dentamaro, Paolo Giglio, Donato Impedovo

    IEEE Transactions on Neural Networks and Learning Systems
    |May 15, 2024
    PubMed
    Summary

    This study introduces adaptive multiscale attention, a novel deep learning technique for tabular data. It enhances performance and provides four levels of explainability for better feature understanding and selection.

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    Area of Science:

    • Computer Science
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Deep learning (DL) excels at analyzing signals like sound and images.
    • DL faces performance limitations with tabular structured data compared to shallow learning.
    • Explainability remains a challenge in deep learning models for tabular data.

    Purpose of the Study:

    • To introduce a novel deep learning architecture for tabular data.
    • To improve the performance of deep learning on classification and regression tasks using tabular data.
    • To enhance the explainability of deep learning models applied to tabular data.

    Main Methods:

    • Developed an adaptive multiscale attention deep neural network architecture.
    • Employed parallel multilevel feature weighting for enhanced pattern recognition.

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  • Implemented four distinct levels of explainability for model comprehension.

    • Main Results:

    • Achieved high F1-scores on seven classification tasks across various dataset sizes.
    • Obtained low mean absolute errors on four regression tasks.
    • Demonstrated the model's ability to learn feature attention and provide interpretable insights.

    Conclusions:

    • Adaptive multiscale attention significantly improves deep learning performance on tabular data.
    • The architecture offers unprecedented explainability, aiding in understanding model behavior.
    • This technique is valuable for feature ranking and selection in tabular datasets.