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        <title>Exploring Kolmogorov Arnold Networks for Interpretable Mental Health Detection and Classification from Social Media Text</title>
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        <namePart type="given">Ajay</namePart>
        <namePart type="given">Surya</namePart>
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    <name type="personal">
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    <abstract>Mental health analysis from social media text demands both high accuracy and interpretability for responsible healthcare applications. This paper explores Kolmogorov Arnold Networks (KANs) for mental health detection and classification, demonstrating their superior performance compared to Multi-Layer Perceptrons (MLPs) in accuracy while requiring fewer parameters. To further enhance interpretability, we leverage the Local Interpretable Model Agnostic Explanations (LIME) method to identify key features, resulting in a simplified KAN model. This allows us to derive governing equations for each class, providing a deeper understanding of the relationships between texts and mental health conditions.</abstract>
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%0 Conference Proceedings
%T Exploring Kolmogorov Arnold Networks for Interpretable Mental Health Detection and Classification from Social Media Text
%A Jampana, Ajay Surya
%A Velagapudi, Mohitha
%A Mohan, Neethu
%A S, Sachin Kumar
%Y Lalitha Devi, Sobha
%Y Arora, Karunesh
%S Proceedings of the 21st International Conference on Natural Language Processing (ICON)
%D 2024
%8 December
%I NLP Association of India (NLPAI)
%C AU-KBC Research Centre, Chennai, India
%F jampana-etal-2024-exploring
%X Mental health analysis from social media text demands both high accuracy and interpretability for responsible healthcare applications. This paper explores Kolmogorov Arnold Networks (KANs) for mental health detection and classification, demonstrating their superior performance compared to Multi-Layer Perceptrons (MLPs) in accuracy while requiring fewer parameters. To further enhance interpretability, we leverage the Local Interpretable Model Agnostic Explanations (LIME) method to identify key features, resulting in a simplified KAN model. This allows us to derive governing equations for each class, providing a deeper understanding of the relationships between texts and mental health conditions.
%U https://aclanthology.org/2024.icon-1.23/
%P 206-214

Markdown (Informal)

[Exploring Kolmogorov Arnold Networks for Interpretable Mental Health Detection and Classification from Social Media Text](https://aclanthology.org/2024.icon-1.23/) (Jampana et al., ICON 2024)

• Exploring Kolmogorov Arnold Networks for Interpretable Mental Health Detection and Classification from Social Media Text (Jampana et al., ICON 2024)

ACL

• Ajay Surya Jampana, Mohitha Velagapudi, Neethu Mohan, and Sachin Kumar S. 2024. Exploring Kolmogorov Arnold Networks for Interpretable Mental Health Detection and Classification from Social Media Text. In Proceedings of the 21st International Conference on Natural Language Processing (ICON), pages 206–214, AU-KBC Research Centre, Chennai, India. NLP Association of India (NLPAI).