Kernel partial least squares regression (KPLS) is a non-linear method for predicting one or more dependent variables from a set of predictors, which transforms the original datasets into a feature space where it is possible to generate a linear model and extract orthogonal factors also called components. A difficulty in implementing KPLS regression is determining the number of components and the kernel function parameters that maximize its performance. In this work, a method is proposed to improve the predictive ability of the KPLS regression by means of memetic algorithms. A metaheuristic tuning procedure is carried out to select the number of components and the kernel function parameters that maximize the cumulative predictive squared correlation coefficient, an overall indicator of the predictive ability of KPLS. The proposed methodology led to estimate optimal parameters of the KPLS regression for the improvement of its predictive ability.

中文翻译：

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模因算法提高了KPLS回归的预测能力

核偏最小二乘回归（KPLS）是一种非线性方法，用于从一组预测变量中预测一个或多个因变量，该方法会将原始数据集转换为特征空间，从而可以生成线性模型并提取正交因子。称为组件。实施KPLS回归的一个困难是确定组件数量和最大化其性能的内核函数参数。在这项工作中，提出了一种通过模因算法提高KPLS回归的预测能力的方法。执行元启发式调整过程，以选择使累积预测平方相关系数最大化的组件数量和核函数参数，KPLS预测能力的总体指标。