A novel ranking method for multiple classifier systems

We introduce an unsupervised optimization method for optimal fusion of multiple classifiers in retrieval problems. The method is based on a ranking loss called the “clarity” index, which does not depend on the label of the test instances. The technique optimizes the weights with which individual classifier scores must be combined to maximize this clarity. Our method is instance-specific; the weights are optimized individually for each test instance. The proposed schema can also be used for instance-specific ranking of classifiers. We also show that the method is highly tolerant to the introduction of noise in classifier outputs.