An Improved Classification Method Using a New Generalized Base Belief Function

Open-world classification must account for samples whose classes are absent from the current frame of discernment. Although generalized evidence theory can represent such possibilities, the conventional base belief function assigns reference support only to nonempty propositions and therefore cannot directly support source-level treatment of unknown information. This work proposes a generalized base belief function that incorporates the open-world proposition into its reference domain and uses it to modify generalized basic probability assignments before fusion. The modified evidence is combined through rules compatible with generalized evidence theory and followed by a known/unknown decision, forming a complete classification procedure for incomplete frames. Experiments on Iris and Seeds show that source-level modification and generalized fusion jointly support known/unknown classification. On Waveform, classification remains stable as controlled perturbation increases in a larger, higher-dimensional, noisy environment. On the Wall-Following Robot dataset, the procedure gives a selective known/unknown decision across different class compositions, preserving known samples while retaining overall discrimination close to GCR. These results support the proposed GBBF-based method for open-world classification under incomplete frames.