Developing predictive models from industrial datasets implies the consideration of many possible predictor variables (features). Using all available features for data-driven modelling is not recommended, as most of them are expected to be irrelevant and their inclusion in the model may compromise robustness and accuracy. In this work, we present, test and compare a new two-stage feature selection method called wide spectrum feature selection for regression (WiSe). In the first stage, a combination of efficient bivariate filters analyzes linear and non-linear association patterns between predictors and responses, screening out clearly noisy features. In the second stage, the reduced set of retained features is subject to further selection in the scope of the predictive methods considered, optimizing their predictive performance. Three simulated datasets and an industrial case illustrate the effectiveness and benefits of applying WiSe to support model development in a wide range of high-dimensional regression problems.

从工业数据集开发预测模型意味着要考虑许多可能的预测变量（特征）。不建议将所有可用功能用于数据驱动的建模，因为大多数功能都是不相关的，并且将它们包含在模型中可能会损害鲁棒性和准确性。在这项工作中，我们介绍，测试和比较一种新的两阶段特征选择方法，称为回归的广谱特征选择（WiSe）。在第一阶段，有效的双变量过滤器的组合分析了预测变量和响应之间的线性和非线性关联模式，从而清晰地筛选出嘈杂的特征。在第二阶段中，减少的保留特征集将在所考虑的预测方法的范围内进行进一步选择，以优化其预测性能。