Using Hierarchical Classification to Exploit Context in Pattern Classification for Information Fusion

Abstract

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.

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