This study presents a comparison between sixteen filter ranking methods applied to a real air pollution problem. Adaptations of the Minimum-Redundancy-Maximum-Relevance (mRMR) algorithm to use the Spearman's rank correlation, the kernel canonical correlation analysis, the Hilbert–Schmidt independence criterion, correntropy, the Pearson's correlation and the distance correlation are included among them. These methods were compared by estimating the hourly NO₂ concentrations at three monitoring stations located in the Bay of Algeciras (Spain). The estimation models were generated using Bayesian regularized artificial neural networks. Different estimation cases were tested for each ranking method. Finally, results were statistically compared to determine which filter ranking strategy produced the best performing model in each case. The proposed estimation scenarios showed how mRMR methods had better results than all the remaining methods when a small number of features was selected. However, their advantage was not so evident when the number of selected features increased. Results from the proposed mRMR methods were promising, especially in the case of the distance correlation mRMR, the kernel canonical correlation analysis mRMR and the Spearman's rank correlation mRMR. These ranking methods performed better than the original mRMR algorithm that employs mutual information internally.

这项研究提出了应用于实际空气污染问题的十六种过滤器分级方法之间的比较。其中包括使用Spearman秩相关，内核规范相关性分析，希尔伯特-施密特独立性准则，熵，皮尔逊相关性和距离相关性对最小冗余最大相关性（mRMR）算法的修改。通过估算每小时的NO ₂来比较这些方法位于阿尔赫西拉斯湾（西班牙）的三个监测站的浓度。估计模型是使用贝叶斯正则化人工神经网络生成的。对于每种排名方法，测试了不同的估计案例。最后，对结果进行统计比较，以确定每种情况下哪种过滤器排名策略可产生最佳性能模型。所提出的估计场景显示时选择的功能少数MRMR方法怎么过比所有其余的方法更好的结果。然而，他们的优势就没有那么明显时，所选要素的数量增加。提出的mRMR方法的结果很有希望，特别是在距离相关mRMR，核标准相关分析mRMR和Spearman秩相关mRMR的情况下。