Feature selection for high-dimensional genomic microarray data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Finding Approximate Repetitions under Hamming Distance
ESA '01 Proceedings of the 9th Annual European Symposium on Algorithms
Mining biomolecular data using background knowledge and artificial neural networks
Handbook of massive data sets
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ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
The feature selection problem: traditional methods and a new algorithm
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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This paper investigates the effects on neural classification performance of biological data by features selection. Where the Relief-F and Symmetrical Tau feature selection algorithms were employed on a set of high level features of DNA and structural profiles. It was observed that even with a small percentage of the features used in neural classifiers, the recognition rate of E.coli promoters was not degraded significantly.