Algorithms for clustering data
Algorithms for clustering data
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Improving Classification by Removing or Relabeling Mislabeled Instances
ISMIS '02 Proceedings of the 13th International Symposium on Foundations of Intelligent Systems
Identifying and Handling Mislabelled Instances
Journal of Intelligent Information Systems
Hit Miss Networks with Applications to Instance Selection
The Journal of Machine Learning Research
Transfer Learning with Data Edit
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Similarity and Kernel Matrix Evaluation Based on Spatial Autocorrelation Analysis
ISMIS '09 Proceedings of the 18th International Symposium on Foundations of Intelligent Systems
SETRED: self-training with editing
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
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We propose a new statistical approach for characterizing the class separability degree in Rp. This approach is based on a nonparametric statistic called "the Cut Edge Weight". We show in this paper the principle and the experimental applications of this statistic. First, we build a geometrical connected graph like the Relative Neighborhood Graph of Toussaint on all examples of the learning set. Second, we cut all edges between two examples of a different class. Third, we calculate the relative weight of these cut edges. If the relative weight of the cut edges is in the expected interval of a random distribution of the labels on all the neighborhood graph's vertices, then no neighborhood-based method will give a reliable prediction model. We will say then that the classes to predict are non-separable.