Computer Information Systems, Missouri State University, MO 65804, USA.
Adv Exp Med Biol. 2010;680:207-13. doi: 10.1007/978-1-4419-5913-3_24.
The most common method of handling automated cell phenotype image classification is to determine a common set of optimal features and then apply standard machine-learning algorithms to classify them. In this chapter, we use advanced methods for determining a set of optimized features for training an ensemble using random subspace with a set of Levenberg-Marquardt neural networks. The process requires that we first run several experiments to determine the individual features that offer the most information. The best performing features are then concatenated and used in the ensemble classification. Applying this approach, we have obtained an average accuracy of 97.4% using the three best benchmarks for this problem: the 2D HeLa dataset and both the endogenous and the transfected LOCATE mouse protein subcellular localization databases.
处理自动化细胞表型图像分类的最常见方法是确定一组常见的最佳特征，然后应用标准的机器学习算法对其进行分类。在本章中，我们使用高级方法来确定一组优化特征，以便使用具有一组 Levenberg-Marquardt 神经网络的随机子空间训练集成。该过程要求我们首先运行几个实验来确定提供最多信息的各个特征。然后将表现最好的特征串联起来，并用于集成分类。应用此方法，我们使用该问题的三个最佳基准（2D HeLa 数据集以及内源性和转染 LOCATE 小鼠蛋白质亚细胞定位数据库）获得了平均 97.4%的准确率。
