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Tuesday, August 4, 2020 | History

8 edition of Computational Methods of Feature Selection (Chapman & Hall/Crc Data Mining and Knowledge Discovery Series) found in the catalog.

Computational Methods of Feature Selection (Chapman & Hall/Crc Data Mining and Knowledge Discovery Series)

  • 88 Want to read
  • 18 Currently reading

Published by Chapman & Hall/CRC .
Written in English

    Subjects:
  • Databases & data structures,
  • Computers,
  • Computers - Data Base Management,
  • Computer Books: Database,
  • Database Management - Database Mining,
  • Information Storage & Retrieval,
  • Computers / Database Management / Data Mining,
  • Number Systems,
  • Probability & Statistics - General,
  • Data mining,
  • Database management,
  • Machine learning

  • Edition Notes

    ContributionsHuan Liu (Editor), Hiroshi Motoda (Editor)
    The Physical Object
    FormatHardcover
    Number of Pages440
    ID Numbers
    Open LibraryOL12313852M
    ISBN 101584888784
    ISBN 109781584888789

    feature selection methods, because data sets may include many challenges such as the huge number of irrelevant and redundant features, noisy data, and high dimensionality in term of features or samples. Therefore, the performance of the feature selection method relies on the performance of the learning method. Conditional mutual information (CMI) maximization is a promising criterion for feature selection in a computationally efficient stepwise way, but it is hard to be applied comprehensively because of imprecise probability calculation and heavy computational load. Many dimension-reduced CMI-based and mutual information (MI)-based methods have been reported to achieve state-of-art .

    Less is more / Huan Liu and Hiroshi Motoda --Unsupervised feature selection / Jennifer G. Dy --Randomized feature selection / David J. Stracuzzi --Causal feature selection / Isabelle Guyon, Constantin Aliferis, and André Elisseeff --Active learning of feature relevance / Emanuele Olivetti, Sriharsha Veeramachaneni, and Paolo Avesani --A study. This research book provides the reader with a selection of high-quality texts dedicated to current progress, new developments and research trends in feature selection for .

      Feature Selection. Inclusion of redundant and noisy features in the model building process would cause poor predictive performance and increased computation. Feature selection is the process of removing irrelevant features and is extremely useful in reducing the dimensionality of the data and improving the predictive accuracy. Huan Liu, Professor Computer Science and Engineering. School of Computing, Informatics, and Decision Systems Engineering. Ira A. Fulton Schools of Engineering, Arizona State University PO Box , Tempe, AZ , U.S.A. Brickyard Suite (CIDSE), South Mill Ave, Tempe, AZ


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Computational Methods of Feature Selection (Chapman & Hall/Crc Data Mining and Knowledge Discovery Series) Download PDF EPUB FB2

Highlighting current research issues, Computational Methods of Feature Selection introduces the basic concepts and principles, state-of-the-art algorithms, and novel applications of this tool. The book begins by exploring unsupervised, randomized, and causal feature selection.

It then reports on some recent results of empowering feature 5/5(2). Highlighting current research issues, Computational Methods of Feature Selection introduces the basic concepts and principles, state-of-the-art algorithms, and novel applications of this tool. The book begins by exploring unsupervised, randomized, and causal feature selection.

Due to increasing demands for dimensionality reduction, research on feature selection has deeply and widely expanded into many fields, including computational statistics, pattern recognition, machine learning, data mining, and knowledge discovery. Highlighting current research issues, Computational Methods of Feature Selection introduces the.

Due to increasing demands for dimensionality reduction, research on feature selection has deeply and widely expanded into many fields, including computational statistics, pattern recognition, machine learning, data mining, and knowledge discovery.

Highlighting current research issues, Computational Methods of Feature Selection introduces theCited by: Derrac J, Cornelis C, García S and Herrera F A preliminary study on the use Computational Methods of Feature Selection book fuzzy rough set based feature selection for improving evolutionary instance selection algorithms Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part I, ().

"Computational Methods of Feature Selection", edited by H. Liu and H. Motoda, two leading experts in the field, collects recent research works from var-ious disciplines on computational meth-ods in feature selection and extrac-tion. The collection reflects the advance-ments in recent years, following the ed-itors’ pioneer book on feature.

Computational Methods of Feature Selection. DOI link for Computational Methods of Feature Selection. Computational Methods of Feature Selection book.

Edited By Huan Liu, Hiroshi Motoda. Edition 1st Edition. First Published eBook Published 29 October Pub. location New York. Imprint Chapman and Hall/CRC. DOI Cited by: Computational Methods of Feature Selection, Huan Liu, Hiroshi Motoda, CRC Press, Boca Raton, FL (), pp, ISBN Article PDF Available.

Relief-F is a feature selection strategy that chooses instances randomly, and changed the weights of the feature relevance based on the nearest neighbor. BibTex entry for: Computational Methods of Feature Selection by Huan Liu and Hiroshi Motoda.

@book{liu, title = {Computational Methods of Feature Selection}, editor = {Liu. Computational Methods of Feature Selection by Huan Liu,available at Book Depository with free delivery worldwide/5(7).

Computational Intelligence and Feature Selection provides readers with the background and fundamental ideas behind Feature Selection (FS), with an emphasis on techniques based on rough and fuzzy sets. For readers who are less familiar with the subject, the book begins with an introduction to fuzzy set theory and fuzzy-rough set theory.

Request PDF | On Jan 1,H Liu and others published Computational Methods of Feature Selection | Find, read and cite all the research you need on ResearchGate. Feature Selection (FS) methods are manual pre-processing steps in machine learning.

• This manuscript introduces a new approach to automate FS in autonomic systems. • The model’s goal is to learn the system baseline, normal behavior, and then evaluate the activity. • This manuscript also provides a taxonomy of current FS techniques.

Firstly, a number of popular feature selection methods are overviewed with objective evaluation on their advantages and disadvantages. Secondly, these methods are grouped into three major classes based on their underlying algorithms. Finally, a variety of. In machine learning and statistics, feature selection, also known as variable selection, attribute selection or variable subset selection, is the process of selecting a subset of relevant features (variables, predictors) for use in model construction.

Feature selection techniques are used for several reasons: simplification of models to make them easier to interpret by researchers/users. Buy Computational Methods of Feature Selection (Chapman & Hall/Crc Data Mining and Knowledge Discovery Series) 1 by Liu, Huan, Motoda, Hiroshi (ISBN: ) from Amazon's Book Store.

Everyday low Reviews: 1. CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): This series aims to capture new developments and applications in data mining and knowledge discovery, while summarizing the computational tools and techniques useful in data analysis.

This series encourages the integration of mathematical, statistical, and computational methods and. Discover data cleaning, feature selection, data transforms, dimensionality reduction and much more in my new book, with 30 step-by-step tutorials and full Python source code.

Overview The focus here is on data preparation for tabular data, e.g. data in the form of a table with rows and columns as it looks in an excel spreadsheet. Feature selection algorithms In this section, we introduce the conventional feature selection algorithm: forward feature selection algorithm; then we explore three greedy variants of the forward algorithm, in order to improve the computational efficiency without sacrificing too much accuracy.

Forward feature selection. Get this from a library. Computational methods of feature selection. [Huan Liu; Hiroshi Motoda;] -- Feature selection is an essential step for successful data mining applications and has practical significance in many areas, such as statistics, pattern.

Final Call for Book Chapters on Computational Methods of Feature Selection. Knowledge discovery and data mining (KDD) is a multidisciplinary effort to extract nuggets of information from data.

Massive data sets have become common in many applications and .BibTeX @MISC{Forman07book:computational, author = {George Forman and George Forman}, title = {Book: Computational Methods of Feature Selection Chapman and Hall/CRC Press, Contents}, year = {}}.12 Computational Methods of Feature Selection tion to feature selection problems, and provide examples.

Finally, the chapter concludes with a discussion of several advanced issues in randomization, and a summary of key points related to the topic. Types of Randomization Randomized algorithms can be divided into two broad classes.

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