A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Attribute selection for modelling
Future Generation Computer Systems - Special double issue on data mining
Use of Contextual Information for Feature Ranking and Discretization
IEEE Transactions on Knowledge and Data Engineering
Machine Learning
Oversearching and layered search in empirical learning
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
On biases in estimating multi-valued attributes
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
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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.