By Huan Liu, Hiroshi Motoda
As a result of expanding calls for for dimensionality relief, examine on function choice has deeply and commonly increased into many fields, together with computational records, development reputation, laptop studying, information mining, and data discovery. Highlighting present examine matters, Computational equipment of characteristic choice introduces the elemental suggestions and ideas, state of the art algorithms, and novel functions of this software.
The ebook starts off through exploring unsupervised, randomized, and causal function choice. It then stories on a few fresh result of empowering function choice, together with energetic function choice, decision-border estimate, using ensembles with self sustaining probes, and incremental function choice. this can be by way of discussions of weighting and native equipment, equivalent to the ReliefF relations, ok -means clustering, neighborhood function relevance, and a brand new interpretation of reduction. The booklet consequently covers textual content category, a brand new characteristic choice ranking, and either constraint-guided and competitive function choice. the ultimate part examines purposes of characteristic choice in bioinformatics, together with characteristic building in addition to redundancy-, ensemble-, and penalty-based characteristic choice.
Through a transparent, concise, and coherent presentation of issues, this quantity systematically covers the major ideas, underlying rules, and creative functions of characteristic choice, illustrating how this robust software can successfully harness monstrous, high-dimensional information and switch it into worthy, trustworthy info.
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Another avenue for research, to aid in defining interestingness, is semi-supervised feature selection. Knowing a few labeled points or constrained must-link and cannot-link pairs can help guide the feature search. Acknowledgment This research was supported by NSF CAREER IIS-0347532. Notes 1 When discussing filters and wrappers, approach, method, and model are used exchangeably. References [1] R. Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan. Automatic subspace clustering of high dimensional data for data mining applications.
Searching for interacting features. In Proceedings of IJCAI - International Joint Conference on AI, January 2007. [18] Z. Zhao and H. Liu. Semi-supervised feature selection via spectral analysis. In Proceedings of SIAM International Conference on Data Mining (SDM-07), 2007. [19] Z. Zhao and H. Liu. Spectral feature selection for supervised and unsupervised learning. In Proceedings of International Conference on Machine Learning, 2007. © 2008 by Taylor & Francis Group, LLC Chapter 2 Unsupervised Feature Selection Jennifer G.
Irrelevant feature and the subset selection problem. In W. Cohen and H. , editors, Machine Learning: Proceedings of the Eleventh International Conference, pages 121–129, New Brunswick, NJ: Rutgers University, 1994. [9] H. Liu and H. Motoda, editors. Feature Extraction, Construction and Selection: A Data Mining Perspective. Boston: Kluwer Academic Publishers, 1998. 2nd Printing, 2001. [10] H. Liu and H. Motoda. Feature Selection for Knowledge Discovery & Data Mining. Boston: Kluwer Academic Publishers, 1998.