High dimensionality is an issue that affects most classification algorithms. This factor implies that the predictive performance of many traditional classifiers decreases considerably as the number of features increases. Therefore, there are numerous proposals that try to mitigate the effects of this issue. This study proposes ClEnDAE, a new classifier based on ensembles whose components incorporate denoising autoencoders (DAEs) to reduce the dimensionality of the input space. On the one hand, the use of ensembles improves the predictive performance by using several components that work jointly. On the other hand, the use of DAEs allows a new higher-level, smaller-sized feature space to be generated, reducing high dimensionality effects. Finally, an experimentation is conducted with the goal of evaluating the behavior of ClEnDAE. The first part of the test compares the performance of ClEnDAE to a model based on basic DAE and to the original untreated data. The second part analyzes the results of ClEnDAE and other traditional methods of dimensionality reduction in order to determine the improvement achieved with the proposed algorithm. In both parts of the experimentation, conclusions show that ClEnDAE offers better predictive performance than the other analyzed models. The main advantage of the ClEnDAE method is the combination of the potential of the ensemble-based methodology, where several components work in parallel, and DAEs, which generate new low-dimensional features that provide more relevant information. Therefore, the classification performance is better than with other classic proposals.

高维是影响大多数分类算法的问题。这个因素意味着许多传统分类器的预测性能会随着特征数量的增加而大大降低。因此，有许多建议试图减轻此问题的影响。这项研究提出了ClEnDAE，这是一种基于集合的新分类器，其组件包含降噪自动编码器（DAE）以减少噪声。输入空间的维数。一方面，使用合奏通过使用几个共同工作的组件可以提高预测性能。另一方面，使用DAE可以生成新的更高级别，更小尺寸的特征空间，从而降低了高维效果。最后，进行了一项旨在评估ClEnDAE行为的实验。测试的第一部分将ClEnDAE的性能与基于基本DAE的模型以及未经处理的原始数据进行了比较。第二部分分析了ClEnDAE和其他传统降维方法的结果，以确定使用该算法实现的改进。在实验的两个部分中，结论表明，ClEnDAE比其他分析模型提供了更好的预测性能。ClEnDAE方法的主要优点是结合了基于集合的方法的潜力（其中多个组件并行工作）和DAE（DAE），DAE生成了新的低维特征，这些特征提供了更多相关信息。因此，分类性能优于其他经典建议。