A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Overcoming the Myopia of Inductive Learning Algorithms with RELIEFF
Applied Intelligence
The Hausdor_ Distance Measure for Feature Selection in Learning Applications
HICSS '99 Proceedings of the Thirty-second Annual Hawaii International Conference on System Sciences-Volume 6 - Volume 6
An introduction to variable and feature selection
The Journal of Machine Learning Research
Margin based feature selection - theory and algorithms
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Feature Selection for Unsupervised Learning
The Journal of Machine Learning Research
The feature selection problem: traditional methods and a new algorithm
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
MCES: A Novel Monte Carlo Evaluative Selection Approach for Objective Feature Selections
IEEE Transactions on Neural Networks
Derivation of an artificial gene to improve classification accuracy upon gene selection
Computational Biology and Chemistry
A novel divide-and-merge classification for high dimensional datasets
Computational Biology and Chemistry
RFS: Efficient feature selection method based on R-value
Computers in Biology and Medicine
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The quality of dataset has a profound effect on classification accuracy, and there is a clear need for some method to evaluate this quality. In this paper, we propose a new dataset evaluation method using the R-value measure. This proposed method is based on the ratio of overlapping areas among categories in a dataset. A high R-value for a dataset indicates that the dataset contains wide overlapping areas among its categories, and classification accuracy on the dataset may become low. We can use the R-value measure to understand the characteristics of a dataset, the feature selection process, and the proper design of new classifiers.