School of Computer Science, Bangor University, LL57 1UT, UK.
Magn Reson Imaging. 2010 May;28(4):583-93. doi: 10.1016/j.mri.2009.12.021. Epub 2010 Jan 21.
Functional magnetic resonance imaging (fMRI) is becoming a forefront brain-computer interface tool. To decipher brain patterns, fast, accurate and reliable classifier methods are needed. The support vector machine (SVM) classifier has been traditionally used. Here we argue that state-of-the-art methods from pattern recognition and machine learning, such as classifier ensembles, offer more accurate classification. This study compares 18 classification methods on a publicly available real data set due to Haxby et al. [Science 293 (2001) 2425-2430]. The data comes from a single-subject experiment, organized in 10 runs where eight classes of stimuli were presented in each run. The comparisons were carried out on voxel subsets of different sizes, selected through seven popular voxel selection methods. We found that, while SVM was robust, accurate and scalable, some classifier ensemble methods demonstrated significantly better performance. The best classifiers were found to be the random subspace ensemble of SVM classifiers, rotation forest and ensembles with random linear and random spherical oracle.
功能磁共振成像（fMRI）正成为一种前沿的脑机接口工具。为了解码大脑模式，需要快速、准确和可靠的分类器方法。支持向量机（SVM）分类器一直被传统使用。在这里，我们认为，来自模式识别和机器学习的最先进方法，如分类器集成，提供了更准确的分类。本研究比较了 18 种分类方法在 Haxby 等人的公开可用真实数据集[Science 293 (2001) 2425-2430]上的性能。该数据来自于一个单一主体的实验，在 10 次运行中组织，每次运行中呈现八类刺激。通过七种流行的体素选择方法选择不同大小的体素子集进行比较。我们发现，虽然 SVM 是鲁棒的、准确的和可扩展的，但一些分类器集成方法表现出了显著更好的性能。发现最好的分类器是 SVM 分类器的随机子空间集成、旋转森林和具有随机线性和随机球形 oracle 的集成。
