a Department of Computing and Numerical Analysis , University of Córdoba , Campus de Rabanales, Albert Einstein Building, Córdoba , Spain.
SAR QSAR Environ Res. 2018 Mar;29(3):187-212. doi: 10.1080/1062936X.2017.1423376.
This paper proposes a method for molecular activity prediction in QSAR studies using ensembles of classifiers constructed by means of two supervised subspace projection methods, namely nonparametric discriminant analysis (NDA) and hybrid discriminant analysis (HDA). We studied the performance of the proposed ensembles compared to classical ensemble methods using four molecular datasets and eight different models for the representation of the molecular structure. Using several measures and statistical tests for classifier comparison, we observe that our proposal improves the classification results with respect to classical ensemble methods. Therefore, we show that ensembles constructed using supervised subspace projections offer an effective way of creating classifiers in cheminformatics.
本文提出了一种使用通过两种有监督子空间投影方法（即非参数判别分析（NDA）和混合判别分析（HDA））构建的分类器集进行 QSAR 研究中分子活性预测的方法。我们使用四个分子数据集和八种不同的分子结构表示模型，研究了与经典集成方法相比，所提出的集成方法的性能。通过使用多个分类器比较的度量和统计检验，我们观察到我们的方法相对于经典集成方法提高了分类结果。因此，我们表明，使用有监督子空间投影构建的集成提供了一种在化学信息学中创建分类器的有效方法。
