Figure 2.

GreedyOpt(C). This algorithm seeks only to include those cost functions which are useful for model quality assessment, while ignoring the others. The result will be simpler if only a few cost function need to be included. The greedy optimization method seeks to identify individual components in C that can improve average correlation and to add them to a pool of useful components, P, only if they can improve average correlation. Initial weights for each cost function are the inverse of the pooled standard deviation of the corresponding cost function.