Ranks underlie outcome of combining classifiers: Quantitative roles for and .

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.