Multivariate Optimization of k for k-Nearest-Neighbor Feature Selection With Dichotomous Outcomes: Complex Associations, Class Imbalance, and Application to RNA-Seq in Major Depressive Disorder.

ABSTRACT: Optimization of nearest-neighbor feature selection depends on the number of samples and features, the type of statistical effect, the feature scoring algorithm, and class imbalance. We recently reported a fixed-k for Nearest-neighbor Projected-Distance Regression (NPDR) that addresses each of these parameters, except for class imbalance. To remedy this, we parameterize our NPDR fixed-k by the minority class size (minority-class-k). We also introduce a class-adaptive fixed-k (hit-miss-k) to improve performance of Relief-based algorithms on imbalanced data. In addition, we present two optimization methods, including constrained variable-wise optimized k (VWOK) and a fixed-k derived with principal components analysis (kPCA), both of which are adaptive to class imbalance. Using simulated data,

SUBMITTER: Dawkins BA 

PROVIDER: S-EPMC12356037 | biostudies-literature | 2025 Jan-Feb

REPOSITORIES: biostudies-literature