Using Hierarchical Classification to Exploit Context in Pattern Classification for Information Fusion
Alex Bailey, Chris J. Harris · ePrints Soton (University of Southampton) · 1999
In data fusion applications it is important that only the minimum set of relevant features are combined at any one stage in the fusion process. A hierarchical classification methodology is described which handles features at different levels of abstraction to produce a more robust and interpretable classifier. This is achieved by dividing the classes into contextual subgroups, which are further divided to produce a tree structure defining relationships between classes. A novel approach is proposed for the class structure design which is formulated as a constrained search in the structure space. This can be performed via a forward search algorithm driven by a cost function dependent on the performance of the class structure and constraints on the required solution.