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.

翻译：机器学习是从多样化数据集中提取有价值信息并进行多种预测的强大工具。传统算法依赖明确界定的输入和输出变量，但在某些场景中，输入与输出变量及其对应的模型底层（输入和输出）层之间的区分是未知的。神经架构搜索（NAS）与特征选择在此类场景中已成为有前景的解决方案。本研究提出IDENAS（基于内部依赖的神经架构搜索方法），将NAS与特征选择相结合。该方法探索完整参数空间中的内部依赖关系，以处理涉及一维传感器数据及二维图像数据的分类任务。IDENAS采用改进的编码器-解码器模型与顺序前向搜索（SFS）算法，将输入输出配置搜索与嵌入式特征选择相结合。实验结果表明，IDENAS相较于其他算法具有更优性能，充分展示了其在模型开发流程与自动化机器学习中的有效性。平均而言，IDENAS实现了显著的建模改进，凸显了其在推进神经架构搜索与特征选择融合领域前沿技术中的重要贡献。