IEEE Trans Neural Netw Learn Syst. 2015 Jan;26(1):35-50. doi: 10.1109/TNNLS.2014.2308902.
We first present a feature selection method based on a multilayer perceptron (MLP) neural network, called feature selection MLP (FSMLP). We explain how FSMLP can select essential features and discard derogatory and indifferent features. Such a method may pick up some useful but dependent (say correlated) features, all of which may not be needed. We then propose a general scheme for dealing with feature selection with "controlled redundancy" (CoR). The proposed scheme, named as FSMLP-CoR, can select features with a controlled redundancy both for classification and function approximation/prediction type problems. We have also proposed a new more effective training scheme named mFSMLP-CoR. The idea is general in nature and can be used with other learning schemes also. We demonstrate the effectiveness of the algorithms using several data sets including a synthetic data set. We also show that the selected features are adequate to solve the problem at hand. Here, we have considered a measure of linear dependency to control the redundancy. The use of nonlinear measures of dependency, such as mutual information, is straightforward. Here, there are some advantages of the proposed schemes. They do not require explicit evaluation of the feature subsets. Here, feature selection is integrated into designing of the decision-making system. Hence, it can look at all features together and pick up whatever is necessary. Our methods can account for possible nonlinear subtle interactions between features, as well as that between features, tools, and the problem being solved. They can also control the level of redundancy in the selected features. Of the two learning schemes, mFSMLP-CoR, not only improves the performance of the system, but also significantly reduces the dependency of the network's behavior on the initialization of connection weights.
我们首先提出了一种基于多层感知器（MLP）神经网络的特征选择方法，称为特征选择 MLP（FSMLP）。我们解释了 FSMLP 如何选择必要的特征，同时丢弃有害和无关的特征。这种方法可能会选择一些有用但相关（例如相关）的特征，而所有这些特征都可能不需要。然后，我们提出了一种处理具有“控制冗余”（CoR）的特征选择的通用方案。所提出的方案，称为 FSMLP-CoR，可用于分类和函数逼近/预测类型问题的特征选择，同时具有受控冗余。我们还提出了一种新的更有效的训练方案，称为 mFSMLP-CoR。该想法本质上是通用的，也可以与其他学习方案一起使用。我们使用包括合成数据集在内的多个数据集来证明算法的有效性。我们还表明，所选特征足以解决手头的问题。在这里，我们考虑了一种线性相关性度量来控制冗余度。使用诸如互信息之类的非线性相关性度量是直截了当的。在这里，所提出的方案具有一些优势。它们不需要显式评估特征子集。在这里，特征选择集成到决策系统的设计中。因此，它可以一起查看所有特征，并选择必要的特征。我们的方法可以考虑特征之间以及特征、工具与要解决的问题之间可能存在的非线性细微交互作用。它们还可以控制所选特征中的冗余水平。在这两种学习方案中，mFSMLP-CoR 不仅提高了系统的性能，而且还显著降低了网络行为对连接权重初始化的依赖性。
