  In this paper we present an unsupervised method to learn the weights with
which the scores of multiple classifiers must be combined in classifier fusion
settings. We also introduce a novel metric for ranking instances based on an
index which depends upon the rank of weighted scores of test points among the
weighted scores of training points. We show that the optimized index can be
used for computing measures such as average precision. Unlike most classifier
fusion methods where a single weight is learned to weigh all examples our
method learns instance-specific weights. The problem is formulated as learning
the weight which maximizes a clarity index; subsequently the index itself and
the learned weights both are used separately to rank all the test points. Our
method gives an unsupervised method of optimizing performance on actual test
data, unlike the well known stacking-based methods where optimization is done
over a labeled training set. Moreover, we show that our method is tolerant to
noisy classifiers and can be used for selecting N-best classifiers.
