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

减轻高维生物医学数据中不可靠类别分配影响的分类方法

• 批准号: 217281-2008
• 负责人: Pizzi, Nicolino
• 金额: $ 0.87万
• 依托单位: University of Manitoba
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2008
• 资助国家: 加拿大
• 起止时间: 2008-01-01 至 2009-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=393197
• 关键词: Classification; methodology; mitigating; effects; unreliable

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 (e.g., a medical expert's analysis of tissue being "normal" or "abnormal"). This process often suffers from three significant challenges: the number of features in a pattern is high; the number of patterns is low; and the reference test may be unreliable. The first two challenges, known collectively as the "curse of dimensionality", cause an inability to find robust, general solutions. This is often addressed by reducing, in some fashion, the number of features; however, a direct correspondence back to the original features is necessary for medical experts to make informed judgments about the mapping's predictive power. While reference tests 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.

对生物医学数据进行分类涉及找到从模式（例如，与某种类型的组织相关的数据）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）表示，并且使用参考测试（例如，医学专家对组织的“正常”或“异常”分析）来分配类别标签。这个过程通常会遇到三个重大挑战：模式中的特征数量很多；图案数量少；并且参考测试可能不可靠。前两个挑战统称为“维度诅咒”，导致无法找到稳健的通用解决方案。这通常可以通过以某种方式减少特征数量来解决；然而，医学专家需要与原始特征直接对应，才能对映射的预测能力做出明智的判断。虽然参考测试可能是完善的基准，但它们很少是完全准确的，有时应用不当。然而，任何弥补这种不精确性的策略都必须确保根据基准正确验证映射。