Instance-Based Learning Algorithms
Machine Learning
Elements of information theory
Elements of information theory
Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Machine Learning
ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development
Data Mining using MLC++, A Machine Learning Library in C++
ICTAI '96 Proceedings of the 8th International Conference on Tools with Artificial Intelligence
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Detection of Frontal Faces in Video Streams
ECCV '02 Proceedings of the International ECCV 2002 Workshop Copenhagen on Biometric Authentication
Infosel++: information based feature selection C++ library
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
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In this work a measure called GD is presented for attribute selection. This measure is defined between an attribute set and a class and corresponds to a generalization of the Mántaras distance that allows to detect the interdependencies between attributes. In the same way, the proposed measure allows to order the attributes by importance in the definition of the concept. This measure does not exhibit a noticeable bias in favor of attributes with many values. The quality of the selected attributes using the GD measure is tested by means of different comparisons with other two attribute selection methods over 19 datasets.