Ordered Weighted Averaging (OWA) operators are widely used as flexible rank-based aggregation mechanisms, including in ensemble classification where class-membership degrees produced by multiple models must be fused into a single decision score. This paper proposes an interpolated OWA operator that replaces the discrete summation by a numerical integration of a continuous surrogate constructed from the rank-wise products w_ix_(i). The surrogate is obtained via interpolation on a normalized rank grid, and the integral is approximated numerically. The proposed operators are evaluated in application to probability fusion in ensemble classification. Three interpolation schemes are investigated: Barycentric polynomial interpolation, cubic splines, and the shape-preserving Piecewise Cubic Hermite Interpolating Polynomial (PCHIP). In addition, a Smooth OWA with trapezoidal smoothing and its interpolated extension are evaluated to separate the effect of local smoothing from interpolation. Experiments were conducted on a heterogeneous benchmark suite including binary and multiclass datasets ranging from 2 to 26 classes, with substantial variation in size and class imbalance. The proposed interpolated OWA yields consistent and frequently statistically significant improvements over the baseline OWA, with the benchmark-level mean accuracy increasing from 90.56% to 90.98% and the largest dataset-level gain reaching approximately 1.56 percentage points. The improvements are also reflected in complementary measures, including macro-F1 and balanced accuracy. Additional analyses show that the interpolation effect is robust across ensemble sizes from 2 to 7, while global Friedman and post-hoc comparisons confirm that the main statistical differences occur between the baseline and the interpolated formulations rather than among the interpolation schemes themselves. The interpolated Smooth OWA variants provide smaller but stable additional gains on top of quadrature-inspired smoothing. A broader comparison with conventional and information-enriched aggregation rules shows that the proposed approach remains competitive without requiring source-specific classifier reliability information. Overall, interpolation-based integration provides a simple and practically effective refinement of rank-based aggregation for ensemble learning.