改进近邻收缩均值算法的阈值方法，减少特征数同时保持分类精度。
Different thresholding methods on Nearest Shrunken Centroid algorithm
- 引入硬阈值和有序阈值替代原有软阈值方法
- 在多类癌症数据集上特征数从2611降至显著更少
- 适合需要简化模型、便于后续分析的研究者
本文研究了不同阈值方法对近邻收缩均值算法（即PAM）的影响。PAM广泛用于高维分类，尤其在微阵列癌症数据分析中表现优异，但其采用软阈值导致保留过多特征——在10个多元分类数据集上平均选择2611个特征，不利于后续研究。问题根源在于软阈值在回归中会产生有偏估计。本文引入硬阈值与有序阈值，并结合深度搜索算法优化阈值参数。通过真实数据与蒙特卡洛模拟对比验证，改进后算法不仅提升癌症状态预测准确率，且生成更简洁模型，特征数量显著减少。
原文摘要 · Abstract (English)
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.
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