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Abstra t. Most evaluation metri s in lassi ation are designed to reward lass uniformity in the example subsets indu ed by a feature (e.g., Information Gain). Other metri s are designed to reward dis rimination power in the ontext of feature sele tion as a means to ombat the feature-intera tion problem (e.g., Relief, Contextual Merit). We de ne a new framework that ombines the strengths of both kinds of metri s. Our framework enri hes the available information when onsidering whi h feature to use to partition the training set. Sin e most metri s rely on only a small fra tion of this information, this framework enlarges the spa e of possible metri s. Experiments on real-world domains in the ontext of de ision-tree learning show how a simple setting for our framework ompares well with standard metri s.