最近收缩中心算法上不同阈值化方法的比较研究
Different thresholding methods on Nearest Shrunken Centroid algorithm
AI总结：
本文针对 PAM 软阈值化保留特征过多的问题，引入硬阈值化、顺序阈值化和深度搜索算法改进最近收缩中心算法，在提升癌症分类准确率的同时显著减少特征数量。
摘要：
本文考察了不同阈值化方法对最近收缩中心算法的影响，该算法在高维分类中通常被称为微阵列预测分析（PAM）。PAM 使用软阈值化以实现高计算效率和高分类准确率，但代价是保留了过多特征。当应用于人类癌症微阵列数据时，PAM 在 10 个多类数据集上平均选择了 2611 个特征。如此大量的特征使得后续研究难以开展。造成这一问题的一个原因是软阈值化，已知它会在回归分析中产生有偏的参数估计。在本文中，我们用另外两种阈值化方法——硬阈值化和顺序阈值化——以及一种深度搜索算法来扩展 PAM 算法，以获得更好的阈值化参数估计。基于真实数据和蒙特卡洛研究，我们对修改后的算法进行了广泛测试并与原始算法进行比较。总体而言，该修改不仅提高了癌症状态预测准确率，还产生了更简约的模型，特征数量显著减少。
英文摘要：
This article considers the impact of different thresholding methods to the Nearest Shrunken Centroid algorithm, which is popularly referred as the Prediction Analysis of Microarrays (PAM) for high-dimensional classification. PAM uses soft thresholding to achieve high computational efficiency and high classification accuracy but in the price of retaining too many features. When applied to microarray human cancers, PAM selected 2611 features on average from 10 multi-class datasets. Such a large number of features make it difficult to perform follow up study. One reason behind this problem is the soft thresholding, which is known to produce biased parameter estimate in regression analysis. In this article, we extend the PAM algorithm with two other thresholding methods, hard and order thresholding, and a deep search algorithm to achieve better thresholding parameter estimate. The modified algorithms are extensively tested and compared to the original one based on real data and Monte Carlo studies. In general, the modification not only gave better cancer status prediction accuracy, but also resulted in more parsimonious models with significantly smaller number of features.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
