Fernández Caballero Juan Carlos, Martínez Francisco José, Hervás César, Gutiérrez Pedro Antonio
Department of Computer Science and Numerical Analysis, University of Córdoba, Córdoba, Spain.
IEEE Trans Neural Netw. 2010 May;21(5):750-70. doi: 10.1109/TNN.2010.2041468. Epub 2010 Mar 11.
This paper proposes a multiclassification algorithm using multilayer perceptron neural network models. It tries to boost two conflicting main objectives of multiclassifiers: a high correct classification rate level and a high classification rate for each class. This last objective is not usually optimized in classification, but is considered here given the need to obtain high precision in each class in real problems. To solve this machine learning problem, we use a Pareto-based multiobjective optimization methodology based on a memetic evolutionary algorithm. We consider a memetic Pareto evolutionary approach based on the NSGA2 evolutionary algorithm (MPENSGA2). Once the Pareto front is built, two strategies or automatic individual selection are used: the best model in accuracy and the best model in sensitivity (extremes in the Pareto front). These methodologies are applied to solve 17 classification benchmark problems obtained from the University of California at Irvine (UCI) repository and one complex real classification problem. The models obtained show high accuracy and a high classification rate for each class.
本文提出了一种使用多层感知器神经网络模型的多分类算法。它试图提升多分类器的两个相互冲突的主要目标：较高的正确分类率水平以及每个类别的高分类率。后一个目标在分类中通常未得到优化，但鉴于在实际问题中需要在每个类别中获得高精度，在此予以考虑。为了解决这个机器学习问题，我们使用基于帕累托的多目标优化方法，该方法基于一种混合进化算法。我们考虑一种基于NSGA2进化算法的混合帕累托进化方法（MPENSGA2）。一旦构建了帕累托前沿，就会使用两种策略或自动个体选择：准确率最高的模型和敏感度最高的模型（帕累托前沿中的极值）。这些方法被应用于解决从加州大学欧文分校（UCI）存储库获得的17个分类基准问题以及一个复杂的实际分类问题。所获得的模型显示出较高的准确率以及每个类别的高分类率。
