Computational Methods Of Feature Selection


Computational Methods Of Feature Selection
DOWNLOAD eBooks

Download Computational Methods Of Feature Selection PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Computational Methods Of Feature Selection book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page





Computational Methods Of Feature Selection


Computational Methods Of Feature Selection
DOWNLOAD eBooks

Author : Huan Liu
language : en
Publisher: CRC Press
Release Date : 2007-10-29

Computational Methods Of Feature Selection written by Huan Liu and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-10-29 with Computers categories.


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 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 selection, including active feature selection, decision-border estimate, the use of ensembles with independent probes, and incremental feature selection. This is followed by discussions of weighting and local methods, such as the ReliefF family, k-means clustering, local feature relevance, and a new interpretation of Relief. The book subsequently covers text classification, a new feature selection score, and both constraint-guided and aggressive feature selection. The final section examines applications of feature selection in bioinformatics, including feature construction as well as redundancy-, ensemble-, and penalty-based feature selection. Through a clear, concise, and coherent presentation of topics, this volume systematically covers the key concepts, underlying principles, and inventive applications of feature selection, illustrating how this powerful tool can efficiently harness massive, high-dimensional data and turn it into valuable, reliable information.



Computational Intelligence And Feature Selection


Computational Intelligence And Feature Selection
DOWNLOAD eBooks

Author : Richard Jensen
language : en
Publisher: John Wiley & Sons
Release Date : 2008-10-03

Computational Intelligence And Feature Selection written by Richard Jensen and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-10-03 with Computers categories.


The rough and fuzzy set approaches presented here open up many new frontiers for continued research and development 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. Building on this foundation, the book provides: A critical review of FS methods, with particular emphasis on their current limitations Program files implementing major algorithms, together with the necessary instructions and datasets, available on a related Web site Coverage of the background and fundamental ideas behind FS A systematic presentation of the leading methods reviewed in a consistent algorithmic framework Real-world applications with worked examples that illustrate the power and efficacy of the FS approaches covered An investigation of the associated areas of FS, including rule induction and clustering methods using hybridizations of fuzzy and rough set theories Computational Intelligence and Feature Selection is an ideal resource for advanced undergraduates, postgraduates, researchers, and professional engineers. However, its straightforward presentation of the underlying concepts makes the book meaningful to specialists and nonspecialists alike.



Encyclopedia Of Machine Learning


Encyclopedia Of Machine Learning
DOWNLOAD eBooks

Author : Claude Sammut
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-03-28

Encyclopedia Of Machine Learning written by Claude Sammut and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-03-28 with Computers categories.


This comprehensive encyclopedia, in A-Z format, provides easy access to relevant information for those seeking entry into any aspect within the broad field of Machine Learning. Most of the entries in this preeminent work include useful literature references.



Computational Complexity


Computational Complexity
DOWNLOAD eBooks

Author : Robert A. Meyers
language : en
Publisher: Springer
Release Date : 2011-10-19

Computational Complexity written by Robert A. Meyers and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-10-19 with Computers categories.


Complex systems are systems that comprise many interacting parts with the ability to generate a new quality of collective behavior through self-organization, e.g. the spontaneous formation of temporal, spatial or functional structures. These systems are often characterized by extreme sensitivity to initial conditions as well as emergent behavior that are not readily predictable or even completely deterministic. The recognition that the collective behavior of the whole system cannot be simply inferred from an understanding of the behavior of the individual components has led to the development of numerous sophisticated new computational and modeling tools with applications to a wide range of scientific, engineering, and societal phenomena. Computational Complexity: Theory, Techniques and Applications presents a detailed and integrated view of the theoretical basis, computational methods, and state-of-the-art approaches to investigating and modeling of inherently difficult problems whose solution requires extensive resources approaching the practical limits of present-day computer systems. This comprehensive and authoritative reference examines key components of computational complexity, including cellular automata, graph theory, data mining, granular computing, soft computing, wavelets, and more.



Artificial Intelligence And Bioinspired Computational Methods


Artificial Intelligence And Bioinspired Computational Methods
DOWNLOAD eBooks

Author : Radek Silhavy
language : en
Publisher: Springer Nature
Release Date : 2020-08-08

Artificial Intelligence And Bioinspired Computational Methods written by Radek Silhavy and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-08-08 with Technology & Engineering categories.


This book gathers the refereed proceedings of the Artificial Intelligence and Bioinspired Computational Methods Section of the 9th Computer Science On-line Conference 2020 (CSOC 2020), held on-line in April 2020. Artificial intelligence and bioinspired computational methods now represent crucial areas of computer science research. The topics presented here reflect the current discussion on cutting-edge hybrid and bioinspired algorithms and their applications.



Feature Engineering And Selection


Feature Engineering And Selection
DOWNLOAD eBooks

Author : Max Kuhn
language : en
Publisher: CRC Press
Release Date : 2019-07-25

Feature Engineering And Selection written by Max Kuhn and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-25 with Business & Economics categories.


The process of developing predictive models includes many stages. Most resources focus on the modeling algorithms but neglect other critical aspects of the modeling process. This book describes techniques for finding the best representations of predictors for modeling and for nding the best subset of predictors for improving model performance. A variety of example data sets are used to illustrate the techniques along with R programs for reproducing the results.



Computational Intelligence For Machine Learning And Healthcare Informatics


Computational Intelligence For Machine Learning And Healthcare Informatics
DOWNLOAD eBooks

Author : Rajshree Srivastava
language : en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date : 2020-06-22

Computational Intelligence For Machine Learning And Healthcare Informatics written by Rajshree Srivastava and has been published by Walter de Gruyter GmbH & Co KG this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-22 with Computers categories.


This book presents a variety of techniques designed to enhance and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics. It is intended to provide a unique compendium of current and emerging machine learning paradigms for healthcare informatics, reflecting the diversity, complexity, and depth and breadth of this multi-disciplinary area.



Advanced Computational Methods For Knowledge Engineering


Advanced Computational Methods For Knowledge Engineering
DOWNLOAD eBooks

Author : Thanh Binh Nguyen
language : en
Publisher: Springer
Release Date : 2016-05-01

Advanced Computational Methods For Knowledge Engineering written by Thanh Binh Nguyen and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-05-01 with Technology & Engineering categories.


This proceedings consists of 20 papers which have been selected and invited from the submissions to the 4th International Conference on Computer Science, Applied Mathematics and Applications (ICCSAMA 2016) held on 2-3 May, 2016 in Laxenburg, Austria. The conference is organized into 5 sessions: Advanced Optimization Methods and Their Applications, Models for ICT applications, Topics on discrete mathematics, Data Analytic Methods and Applications and Feature Extractio, respectively. All chapters in the book discuss theoretical and practical issues connected with computational methods and optimization methods for knowledge engineering. The editors hope that this volume can be useful for graduate and Ph.D. students and researchers in Applied Sciences, Computer Science and Applied Mathematics.



Advances In Artificial Intelligence Sbia 2012


Advances In Artificial Intelligence Sbia 2012
DOWNLOAD eBooks

Author : Leliane N. Barros
language : en
Publisher: Springer
Release Date : 2012-10-05

Advances In Artificial Intelligence Sbia 2012 written by Leliane N. Barros and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-10-05 with Computers categories.


This book constitutes the refereed proceedings of the 21st Brazilian Symposium on Artificial Intelligence, SBIA 2012, held in Curitiba, Brazil, in October 2012. The 23 revised full papers presented were carefully reviewed and selected from 81 submissions. The papers cover the following topics: knowledge representation, machine learning, machine learning and computer vision, agent-based and multi-agent systems, robotics and language, as well as constraints.



Recent Advances In Ensembles For Feature Selection


Recent Advances In Ensembles For Feature Selection
DOWNLOAD eBooks

Author : Verónica Bolón-Canedo
language : en
Publisher: Springer
Release Date : 2018-04-30

Recent Advances In Ensembles For Feature Selection written by Verónica Bolón-Canedo and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-04-30 with Technology & Engineering categories.


This book offers a comprehensive overview of ensemble learning in the field of feature selection (FS), which consists of combining the output of multiple methods to obtain better results than any single method. It reviews various techniques for combining partial results, measuring diversity and evaluating ensemble performance. With the advent of Big Data, feature selection (FS) has become more necessary than ever to achieve dimensionality reduction. With so many methods available, it is difficult to choose the most appropriate one for a given setting, thus making the ensemble paradigm an interesting alternative. The authors first focus on the foundations of ensemble learning and classical approaches, before diving into the specific aspects of ensembles for FS, such as combining partial results, measuring diversity and evaluating ensemble performance. Lastly, the book shows examples of successful applications of ensembles for FS and introduces the new challenges that researchers now face. As such, the book offers a valuable guide for all practitioners, researchers and graduate students in the areas of machine learning and data mining.