Abstract
Two binary labelling techniques for decision-level fusion are considered for reducing correlation in the context of multiple classifier systems. First, we describe a method based on error correcting coding that uses binary code words to decompose a multi-class problem into a set of complementary two-class problems. We look at the conditions necessary for reduction of error and introduce a modified version that is less sensitive to code word selection. Second, we describe a partitioning method for two-class problems that transforms each training pattern into a vertex of the binary hypercube. A constructive algorithm for binary-to-binary mappings identifies a set of inconsistently classified patterns, random subsets of which are used to perturb base classifier training sets. Experimental results on artificial and real data, using a combination of simple neural network classifiers, demonstrate improvement in performance for these techniques, the first suitable for
k-class problems,
k>2 and the second for
k=2.