Yang Jian-Bo, Shen Kai-Quan, Ong Chong-Jin, Li Xiao-Ping
Department of Mechanical Engineering, National University of Singapore, Singapore.
IEEE Trans Neural Netw. 2009 Dec;20(12):1911-22. doi: 10.1109/TNN.2009.2032543. Epub 2009 Oct 9.
This paper presents a new wrapper-based feature selection method for multilayer perceptron (MLP) neural networks. It uses a feature ranking criterion to measure the importance of a feature by computing the aggregate difference, over the feature space, of the probabilistic outputs of the MLP with and without the feature. Thus, a score of importance with respect to every feature can be provided using this criterion. Based on the numerical experiments on several artificial and real-world data sets, the proposed method performs, in general, better than several selected feature selection methods for MLP, particularly when the data set is sparse or has many redundant features. In addition, as a wrapper-based approach, the computational cost for the proposed method is modest.
本文提出了一种用于多层感知器（MLP）神经网络的基于包装器的新特征选择方法。它使用一种特征排序标准，通过计算在有该特征和无该特征情况下MLP概率输出在特征空间上的累积差异，来衡量一个特征的重要性。因此，使用该标准可以为每个特征提供一个重要性得分。基于在多个人工和真实数据集上的数值实验，总体而言，所提出的方法比为MLP选择的几种特征选择方法表现更好，特别是当数据集稀疏或有许多冗余特征时。此外，作为一种基于包装器的方法，所提出方法的计算成本适中。
