Linear combination is a popular approach in information fusion due to its simplicity. However, it suffers from the performance upper-bound of linearity and dependency on the selection of weights. In this study, we introduce a 'simple' alternative for linear combination, which is a non-linear extension on it. The approach is based on the Analytical Network Process, which is a popular approach in Operational Research, but never applied for fusion before. The approach benefits from two major ideas; interdependency between classes and dependency of classes on the features. Experiments conducted on CCV dataset demonstrate that proposed approach outperforms linear combination and other simple approaches, moreover it is less-dependent on the selection of weights.