Interpretable Deep Learning for Pulsar Star Classification with Explainable AI Techniques: A Comparative Analysis of TabNet and FT-Transformer Models

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dc.authoridAsal, Bur�ak/0009-0003-3729-8170
dc.contributor.authorAsal, Burcak
dc.date.accessioned2026-02-27T07:33:28Z
dc.date.available2026-02-27T07:33:28Z
dc.date.issued2025
dc.description33rd Conference on Signal Processing and Communications Applications-SIU-Annual
dc.description.abstractThe classification of pulsar stars from vast astronomical datasets is an important task in astrophysics, which aids in the discovery of these rapidly rotating neutron stars that emit periodic radio signals. Traditional machine learning techniques have demonstrated effectiveness in pulsar detection but often can lack the capacity to handle complex feature interactions within structured data. Deep learning models, especially designed for tabular data, have emerged as promising alternatives. In this study, TabNet and FT-Transformer, two advanced deep learning architectures specifically designed for tabular data are explored to enhance pulsar star classification on the HTRU2 dataset. Additionally, to improve model transparency, two explainable artificial intelligence (XAI) techniques, which are SHAP (SHapley Additive Explanations) and LIME (Local Interpretable Model-Agnostic Explanations), are integrated to analysis both global and local feature contributions in the classification process.
dc.identifier.doi10.1109/SIU66497.2025.11112040
dc.identifier.isbn979-8-3315-6656-2; 979-8-3315-6655-5
dc.identifier.issn2165-0608
dc.identifier.urihttp://dx.doi.org/10.1109/SIU66497.2025.11112040
dc.identifier.urihttps://hdl.handle.net/20.500.14669/4598
dc.identifier.wosWOS:001575462500157
dc.indekslendigikaynakWeb of Science
dc.language.isotr
dc.publisherIEEE
dc.relation.ispartof2025 33rd Signal Processing and Communications Applications Conference, Siu
dc.relation.ispartofseriesSignal Processing and Communications Applications Conference
dc.relation.publicationcategoryKonferans ��esi - Uluslararas� - Kurum ��retim Eleman�
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20260302
dc.subjectPulsar Classification
dc.subjectTabNet
dc.subjectFT-Transformer
dc.subjectDeep Learning
dc.subjectMachine Learning
dc.subjectExplainable AI (XAI)
dc.subjectSHAP
dc.subjectLIME
dc.titleInterpretable Deep Learning for Pulsar Star Classification with Explainable AI Techniques: A Comparative Analysis of TabNet and FT-Transformer Models
dc.typeProceedings Paper

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