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首页> 外文期刊>Neuroinformatics >Fast, Accurate, and Stable Feature Selection Using Neural Networks
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Fast, Accurate, and Stable Feature Selection Using Neural Networks

机译：使用神经网络快速，准确，稳定的特征选择

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摘要

Multi-voxel pattern analysis often necessitates feature selection due to the high dimensional nature of neuroimaging data. In this context, feature selection techniques serve the dual purpose of potentially increasing classification accuracy and revealing sets of features that best discriminate between classes. However, feature selection techniques in current, widespread use in the literature suffer from a number of deficits, including the need for extended computational time, lack of consistency in selecting features relevant to classification, and only marginal increases in classifier accuracy. In this paper we present a novel method for feature selection based on a single-layer neural network which incorporates cross-validation during feature selection and stability selection through iterative subsampling. Comparing our approach to popular alternative feature selection methods, we find increased classifier accuracy, reduced computational cost and greater consistency with which relevant features are selected. Furthermore, we demonstrate that importance mapping, a technique used to identify voxels relevant to classification, can lead to the selection of irrelevant voxels due to shared activation patterns across categories. Our method, owing to its relatively simple architecture, flexibility and speed, can provide a viable alternative for researchers to identify sets of features that best discriminate classes.
机译：多体素图案分析通常需要由于神经影像数据数据的高维性质而导致的特征选择。在这种情况下，特征选择技术有助于潜在地增加分类准确性和揭示一组最佳区分类别的特征的双重目的。然而，在文献中的当前功能中的特征选择技术遭受许多缺陷，包括对扩展计算时间的需求，选择与分类相关的特征时缺乏一致性，并且仅在分类器精度中的边际增加。在本文中，我们介绍了一种基于单层神经网络的特征选择的新方法，该特征选择通过迭代分配在特征选择和稳定性选择期间结合交叉验证。将我们的方法与流行替代特征选择方法进行比较，我们发现增加的分类器精度，减少了计算成本和更大的一致性，选择了相关的功能。此外，我们证明了重要的映射，用于识别与分类相关的体素的技术可以导致由于跨类别的共享激活模式而导致无关的体素。我们的方法，由于其架构相对简单，灵活性和速度，可以为研究人员提供可行的替代方案，以识别最佳区分类的功能。

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