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.

Show description

Read Online or Download Computational methods of feature selection PDF

Best computational mathematicsematics books

Read e-book online A new table of seven-place logarithms PDF

It is a pre-1923 ancient copy that was once curated for caliber. caliber insurance used to be performed on each one of those books in an try and eliminate books with imperfections brought via the digitization strategy. although we have now made top efforts - the books can have occasional error that don't bog down the analyzing event.

Download e-book for kindle: Hybrid Systems: Computation and Control: Second by Philippe Baufreton (auth.), Frits W. Vaandrager, Jan H. van

This quantity includes the lawsuits of the second one overseas Workshop on Hybrid structures: Computation and regulate (HSCC’99) to be held March 29- 31, 1999, within the village Berg en Dal close to Nijmegen, The Netherlands. The rst workshop of this sequence used to be held in April 1998 on the college of California at Berkeley.

Download e-book for kindle: Applied Shape Optimization for Fluids, Second Edition by Bijan Mohammadi, Olivier Pironneau

Computational fluid dynamics (CFD) and optimum form layout (OSD) are of functional significance for lots of engineering purposes - the aeronautic, vehicle, and nuclear industries are all significant clients of those applied sciences. Giving the state-of-the-art suit optimization for a longer diversity of purposes, this re-creation explains the equations had to comprehend OSD difficulties for fluids (Euler and Navier Strokes, but additionally these for microfluids) and covers numerical simulation ideas.

Extra resources for Computational methods of feature selection

Example text

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.

Download PDF sample

Computational methods of feature selection by Huan Liu, Hiroshi Motoda


by Daniel
4.0

Rated 4.36 of 5 – based on 16 votes