Classification methodology for mitigating the effects of unreliable class assignments in high dimensional biomedical data

对生物医学数据进行分类涉及找到从模式(例如，与某种类型的组织有关的数据)到一组类别(例如，疾病状态)的映射(关系)。模式由特征(例如，生物化合物的浓度)来表示，并且使用参考测试(RT)(例如，医学专家对组织的分析是“正常”还是“异常”)来分配类别标签。这一过程通常面临三个重大挑战：RT可能不可靠；模式的数量很少；模式中的特征数量很高。虽然RTS可能是久负盛名的基准，但它们很少是完全准确的，有时应用不当。然而，任何补偿这种不精确的策略都必须确保根据基准正确地验证映射。最后两个挑战，统称为“维度诅咒”，导致无法找到健壮的、通用的解决方案。这通常是通过选择最具歧视性的特征子集的转换来解决的，而不会阻碍医学专家对映射的预测能力做出知情判断的能力。

Classifying biomedical data involves finding a mapping (relationship) from patterns (e.g., data relating to some type of tissue) to a set of classes (e.g., disease states). Patterns are represented by features (e.g., concentrations of biological compounds) and class labels are assigned using a reference test (RT) (e.g., a medical expert's analysis of tissue being "normal" or "abnormal"). This process often suffers from three significant challenges: the RT may be unreliable; the number of patterns is low; and the number of features in a pattern is high. While RTs may be well-established benchmarks, they are seldom perfectly accurate and sometimes improperly applied. Nevertheless, any strategy that compensates for this imprecision must ensure that the mapping is correctly validated against the benchmark. The last two challenges, known collectively as the "curse of dimensionality", cause an inability to find robust, general solutions. This is often addressed by transformations that select the most discriminatory subset of features while not impeding the medical expert's ability to make informed judgments about the mapping's predictive power.