Most evaluation metrics in classification are designed to reward class uniformity in the example subsets induced by a feature (e.g., Information Gain). Other metrics are designed to reward discrimination power in the context of feature selection as a means to combat the feature-interaction problem (e.g., Relief, Contextual Merit). We define a new framework that combines the strengths of both kinds of metrics. Our framework enriches the available information when considering which feature to use to partition the training set. Since most metrics rely on only a small fraction of this information, this framework enlarges the space of possible metrics. Experiments on real-world domains in the context of decision-tree learning show how a simple setting for our framework compares well with standard metrics.