Read e-book online Feature Selection and Ensemble Methods for Bioinformatics: PDF

By Oleg Okun

ISBN-10: 1609605578

ISBN-13: 9781609605575

ISBN-10: 1609605586

ISBN-13: 9781609605582

Laptop studying is the department of synthetic intelligence whose target is to advance algorithms that upload studying functions to desktops. Ensembles are a vital part of laptop studying. a customary ensemble contains a number of algorithms appearing the duty of prediction of the category label or the measure of sophistication club for a given enter offered as a collection of measurable features, referred to as good points. characteristic choice and Ensemble tools for Bioinformatics: Algorithmic class and Implementations bargains a special standpoint on desktop studying elements of microarray gene expression dependent melanoma category. This multidisciplinary textual content is on the intersection of laptop technology and biology and, for this reason, can be utilized as a reference e-book by means of researchers and scholars from either fields. every one bankruptcy describes the method of set of rules layout from commencing to finish and goals to notify readers of top practices to be used of their personal learn.

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Ford, J. , & Pearlman, J. (2005). HykGene: a hybrid approach for selecting marker genes for phenotype classification using microarray gene expression data. Bioinformatics (Oxford, England), 21(8), 1530–1537. 1093/bioinformatics/bti192 Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

Cancer Cell, 1(2), 203–209. -N. (2009). kNN: k-nearest neighbors. In X. Wu, & V. ), The top ten algorithms in data mining (pp. 151-162). Boca Raton, FL: Chapman & Hall/CRC Press. , & Tibshirani, R. (2001). Missing value estimation methods for DNA microarrays. Bioinformatics (Oxford, England), 17(6), 520–525. , & Bengio, Y. (2002). K-local hyperplane and convex distance nearest neighbor algorithms. In Dietterich, T. , & Ghahramani, Z. ), Advances in Neural Information Processing Systems, 14 (pp.

Sahami, M. (1995). Supervised and unsupervised discretization of continuous features. In A. Prieditis, & S. J. ), Proceedings of the 12th International Conference on Machine Learning, Tahoe City, CA (pp. 194-202). San Francisco, CA: Morgan Kaufmann. Everitt, B. (2006). ). Cambridge, UK: Cambridge University Press. Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861–874. 010 Fayyad, U. , & Irani, K. B. (1993). Multi-interval discretization of continuousvalued attributes for classification learning.

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Feature Selection and Ensemble Methods for Bioinformatics: Algorithmic Classification and Implementations by Oleg Okun


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