Download PDF by Huan Liu, Hiroshi Motoda: Computational methods of feature selection

By Huan Liu, Hiroshi Motoda

ISBN-10: 1584888784

ISBN-13: 9781584888789

ISBN-10: 1584888792

ISBN-13: 9781584888796

As a result of expanding calls for for dimensionality relief, study on function choice has deeply and greatly accelerated into many fields, together with computational facts, trend attractiveness, laptop studying, facts mining, and information discovery. Highlighting present study matters, Computational tools of function choice introduces the fundamental thoughts and rules, state of the art algorithms, and novel functions of this instrument.

The ebook starts through exploring unsupervised, randomized, and causal function choice. It then studies on a few contemporary result of empowering characteristic choice, together with energetic function choice, decision-border estimate, using ensembles with self sufficient probes, and incremental function choice. this is often via discussions of weighting and native tools, reminiscent of the ReliefF relatives, ok -means clustering, neighborhood characteristic relevance, and a brand new interpretation of aid. The ebook therefore covers textual content category, a brand new function choice ranking, and either constraint-guided and competitive function choice. the ultimate part examines purposes of function choice in bioinformatics, together with function building in addition to redundancy-, ensemble-, and penalty-based function choice.

Through a transparent, concise, and coherent presentation of themes, this quantity systematically covers the major suggestions, underlying rules, and artistic purposes of characteristic choice, illustrating how this robust instrument can successfully harness significant, high-dimensional information and switch it into beneficial, trustworthy info.

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Extra resources for Computational methods of feature selection

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Applying Randomization to Feature Selection . . . . . . . . . . . . . The Role of Heuristics . . . . . . . . . . . . . . . . . . . . . . . . . . Examples of Randomized Selection Algorithms . . . . . . . . . . . . . Issues in Randomization . . . . . . . . . . . . . . . . . . . . . . . . . Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2008 by Taylor & Francis Group, LLC 36 Computational Methods of Feature Selection [4] P. S. Bradley and U. M. Fayyad. Refining initial points for K-means clustering. In Proceedings of the Fifteenth International Conference on Machine Learning, pages 91–99, San Francisco, CA, Morgan Kaufmann, 1998. [5] Y. Censor and S. Zenios. Parallel Optimization: Theory, Algorithms, and Applications. Oxford University Press, 1998. -W. -S. Jin. A new cell-based clustering method for large, high-dimensional data in data mining applications.

Hyv¨ arinen. Survey on independent component analysis. Neural Computing Surveys, 2:94–128, 1999. [29] A. K. Jain, M. N. Murty, and P. J. Flynn. Data clustering: A review. ACM Computing Surveys, 31(3):264–323, 1999. [30] I. Jolliffe. Principal Component Analysis. Springer, New York, Second © 2008 by Taylor & Francis Group, LLC 38 Computational Methods of Feature Selection edition edition, 2002. [31] Y. S. Kim, N. Street, and F. Menczer. Evolutionary model selection in unsupervised learning. Intelligent Data Analysis, 6:531–556, 2002.

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Computational methods of feature selection by Huan Liu, Hiroshi Motoda

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