The problem of constructing a single classifier when multiple phenomenologies are measured by different sensor types is made more difficult because features take diversified forms, and classifiers built from them have variable performance. For example, features can be continuous or binary valued (as in discrete labels), or be composed of incompatible structural primitives. Therefore, it is difficult to lump all of these features together into a single classifier for decision making. This realization leads to the combined use of multiple classifiers.
The solution presented in this paper describes the formulation and development of:
A computational procedure for computing approximate hyperplane decision boundaries to achieve a balanced classifier.
Achieving a minimum Bayes-risk balanced classifier as a convex combination of balanced classifiers. This is done for both independent and correlated cases.
Convex combinations of balanced classifiers are balanced. However, our research has further generalized this concept by computing optimal convex combinations of classifiers so as to also attain the property of being minimum Bayes-risk for the combined classifier. The principle exploited was to incorporate either the decisions or the decision statistics of the individual classifiers within a combined confusion matrix considering both the correlated and independent cases. This was posed as an optimization problem to be approached via Markov-Chain Monte Carlo methods. Some preliminary results are shown.