Abstract
High throughput data domains such as proteomics and expression mining frequently challenge data mining algorithms with unprecedented dimensionality and size. High dimensionality has detrimental effects on the performance of data mining algorithms. Many dimensionality reduction techniques, including feature ranking and selection, have been used to diminish the curse of dimensionality. Protein classification and feature ranking are both classical problem domains, which have been explored extensively in the past. We propose a data mining based algorithm to address the problem of ranking and selecting feature descriptors for the physiochemical properties of a protein, which are generally used for discriminative method protein classification. We present a novel data shrinking-based method of ranking and feature descriptor selection for physiochemical properties. The proposed methodology is employed to demonstrate the discriminative power of top ranked features for protein structural classification. Our experimental study shows that our top ranked feature descriptors produce competitive and superior classification results.
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Dua, S., Saini, S. (2009). Data Shrinking Based Feature Ranking for Protein Classification. In: Prasad, S.K., Routray, S., Khurana, R., Sahni, S. (eds) Information Systems, Technology and Management. ICISTM 2009. Communications in Computer and Information Science, vol 31. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00405-6_10
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DOI: https://doi.org/10.1007/978-3-642-00405-6_10
Publisher Name: Springer, Berlin, Heidelberg
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