تقرير
IDENAS: Internal Dependency Exploration for Neural Architecture Search
| العنوان: | IDENAS: Internal Dependency Exploration for Neural Architecture Search |
|---|---|
| المؤلفون: | Hoang, Anh T., Viharos, Zsolt J. |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Neural and Evolutionary Computing, 68T07, 68T10, 93A10, I.5.2, I.5.1, I.2.6 |
| الوصف: | Machine learning is a powerful tool for extracting valuable information and making various predictions from diverse datasets. Traditional algorithms rely on well-defined input and output variables however, there are scenarios where the distinction between the input and output variables and the underlying, associated (input and output) layers of the model, are unknown. Neural Architecture Search (NAS) and Feature Selection have emerged as promising solutions in such scenarios. This research proposes IDENAS, an Internal Dependency-based Exploration for Neural Architecture Search, integrating NAS with feature selection. The methodology explores internal dependencies in the complete parameter space for classification involving 1D sensor and 2D image data as well. IDENAS employs a modified encoder-decoder model and the Sequential Forward Search (SFS) algorithm, combining input-output configuration search with embedded feature selection. Experimental results demonstrate IDENASs superior performance in comparison to other algorithms, showcasing its effectiveness in model development pipelines and automated machine learning. On average, IDENAS achieved significant modelling improvements, underscoring its significant contribution to advancing the state-of-the-art in neural architecture search and feature selection integration. Comment: 57 pages, 19 figures + appendix, the related software code can be found under the link: https://github.com/viharoszsolt/IDENAS |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2310.17250 |
| رقم الأكسشن: | edsarx.2310.17250 |
| قاعدة البيانات: | arXiv |
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