Audiology entities are using Machine Learning (ML) models to guide their screening towards people at risk. Feature Engineering (FE) focuses on optimizing data for ML models, with evolutionary methods being effective in feature selection and construction tasks. This work aims to benchmark an evolutionary FE wrapper, using models based on decision trees as proxies. The FEDORA framework is applied to a Hearing Loss (HL) dataset, being able to reduce data dimensionality and statistically maintain baseline performance. Compared to traditional methods, FEDORA demonstrates superior performance, with a maximum balanced accuracy of 76.2%, using 57 features. The framework also generated an individual that achieved 72.8% balanced accuracy using a single feature.

翻译：听力学机构正利用机器学习（ML）模型引导对有风险人群进行筛查。特征工程（FE）专注于为ML模型优化数据，其中进化方法在特征选择与构建任务中表现优异。本研究旨在以决策树模型为代理，对进化式FE封装器进行基准测试。将FEDORA框架应用于听力损失（HL）数据集，该框架能够降低数据维度并在统计上保持基线性能。与传统方法相比，FEDORA展现出更优性能，在使用57个特征时达到76.2%的最大平衡准确率。该框架还生成了一个仅使用单一特征即实现72.8%平衡准确率的个体方案。