A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Pattern classification with compact distribution maps
Computer Vision and Image Understanding
Input Feature Selection by Mutual Information Based on Parzen Window
IEEE Transactions on Pattern Analysis and Machine Intelligence
Theoretical and Empirical Analysis of ReliefF and RReliefF
Machine Learning
An introduction to variable and feature selection
The Journal of Machine Learning Research
Fast Branch & Bound Algorithms for Optimal Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Input Variable Selection: Mutual Information and Linear Mixing Measures
IEEE Transactions on Knowledge and Data Engineering
A Branch and Bound Algorithm for Feature Subset Selection
IEEE Transactions on Computers
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
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This paper proposes a new feature selection algorithm. First, the data at every attribute are sorted. The continuously distributed data with the same class labels are grouped into runs. The runs whose length is greater than a given threshold are selected as “valid” runs, which enclose the instances separable from the other classes. Second, we count how many runs cover every instance and check how the covering number changes once eliminate a feature. Then, we delete the feature that has the least impact on the covering cases for all instances. We compare our method with ReliefF and a method based on mutual information. Evaluation was performed on 3 image databases. Experimental results show that the proposed method outperformed the other two.