Sniatynski Matthew J, Shepherd John A, Ernst Thomas, Wilkens Lynne R, Hsu D Frank, Kristal Bruce S
Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, 221 Longwood Avenue, LM322B, Boston, MA 02115, USA.
Division of Sleep Medicine, Harvard Medical School, Boston, MA 02115, USA.
Patterns (N Y). 2021 Dec 22;3(2):100415. doi: 10.1016/j.patter.2021.100415. eCollection 2022 Feb 11.
Combining classifier systems potentially improves predictive accuracy, but outcomes have proven impossible to predict. Classification most commonly improves when the classifiers are "sufficiently good" (generalized as " ") and "sufficiently different" (generalized as " "), but the individual and joint quantitative influence of these factors on the final outcome remains unknown. We resolve these issues. Beginning with simulated data, we develop the DIRAC framework ( of Ranks and ), which accurately predicts outcome of both score-based fusions originating from exponentially modified Gaussian distributions and rank-based fusions, which are inherently distribution independent. DIRAC was validated using biological dual-energy X-ray absorption and magnetic resonance imaging data. The DIRAC framework is domain independent and has expected utility in far-ranging areas such as clinical biomarker development/personalized medicine, clinical trial enrollment, insurance pricing, portfolio management, and sensor optimization.
组合分类器系统可能会提高预测准确性，但结果已证明无法预测。当分类器“足够好”（概括为“ ”）且“足够不同”（概括为“ ”）时，分类通常会得到改善，但这些因素对最终结果的个体和联合定量影响仍然未知。我们解决了这些问题。从模拟数据开始，我们开发了DIRAC框架（秩和的 ），它能准确预测源自指数修正高斯分布的基于分数的融合以及本质上与分布无关的基于秩的融合的结果。DIRAC使用生物双能X射线吸收和磁共振成像数据进行了验证。DIRAC框架与领域无关，在临床生物标志物开发/个性化医疗、临床试验入组、保险定价、投资组合管理和传感器优化等广泛领域具有预期效用。
