IEA/AIE '02 Proceedings of the 15th international conference on Industrial and engineering applications of artificial intelligence and expert systems: developments in applied artificial intelligence
Automatic digital modulation recognition using artificial neural network and genetic algorithm
Signal Processing - Special issue on independent components analysis and beyond
Feature Selection via Maximizing Neighborhood Soft Margin
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
Selecting discrete and continuous features based on neighborhood decision error minimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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In their previous work, the authors have developed a method for selecting features based on the analysis of class regions approximated by hyperboxes. They select features analyzing class regions approximated by ellipsoids. First, for a given set of features, each class region is approximated by an ellipsoid with the center and the covariance matrix calculated by the data belonging to the class. Then, similar to their previous work, the exception ratio is defined to represent the degree of overlaps in the class regions approximated by ellipsoids. From the given set of features, they temporally delete each feature, one at a time, and calculate the exception ratio. Then, the feature whose associated exception ratio is the minimum is deleted permanently. They iterate this procedure while the exception ratio or its increase is within a specified value by feature deletion. The simulation results show that the current method is better than the principal component analysis (PCA) and performs better than the previous method, especially when the distributions of class data are not parallel to the feature axes