Hierarchical Fusion of Multiple Classifiers for Hyperspectral Data Analysis

: Many classification problems involve high dimensional inputs and a large number of classes. Multiclassifier fusion approaches to such difficult problems typically centre around smart feature extraction, input resampling methods, or input space partitioning to exploit modular learning. In this paper, we investigate how partitioning of the output space (i.e. the set of class labels) can be exploited in a multiclassifier fusion framework to simplify such problems and to yield better solutions. Specifically, we introduce a hierarchical technique to recursively decompose a C-class problem into C 1 two-(meta) class problems. A generalised modular learning framework is used to partition a set of classes into two disjoint groups called meta-classes. The coupled problems of finding a good partition and of searching for a linear feature extractor that best discriminates the resulting two meta-classes are solved simultaneously at each stage of the recursive algorithm. This results in a binary t...

• Type: Journal
• Year: 2002
• Where: PAA
• Authors: Shailesh Kumar, Joydeep Ghosh, Melba M. Crawford
• Keywords: Classification Problems, Distributed And Parallel Computing, Multiclassifier Fusion, PAA 2002, Problems