Tree structure for efficient data mining using rough sets
Pattern Recognition Letters - Special issue: Rough sets, pattern recognition and data mining
On the Scalability of Genetic Algorithms to Very Large-Scale Feature Selection
Real-World Applications of Evolutionary Computing, EvoWorkshops 2000: EvoIASP, EvoSCONDI, EvoTel, EvoSTIM, EvoROB, and EvoFlight
Prefix-suffix trees: a novel scheme for compact representation of large datasets
PReMI'07 Proceedings of the 2nd international conference on Pattern recognition and machine intelligence
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In statistical pattern recognition, the decision of which features to use is usually left to human judgment. If possible, automatic methods are desirable. Like multilayer perceptrons, learning subspace methods (LSMs) have the potential to integrate feature extraction and classification. In this paper, we propose two new algorithms, along with their neural-network implementations, to overcome certain limitations of the earlier LSMs. By introducing one cluster at a time and adapting it if necessary, we eliminate one limitation of deciding how many clusters to have in each class by trial-and-error. By using the principal component analysis neural networks along with this strategy, we propose neural-network models which are better in overcoming another limitation, scalability. Our results indicate that the proposed classifiers are comparable to classifiers like the multilayer perceptrons and the nearest-neighbor classifier in terms of classification accuracy. In terms of classification speed and scalability in design, they appear to be better for large-dimensional problems