Hierarchical Feature Selection for Knowledge Discovery Book [PDF] Download

Download the fantastic book titled Hierarchical Feature Selection for Knowledge Discovery written by Cen Wan, available in its entirety in both PDF and EPUB formats for online reading. This page includes a concise summary, a preview of the book cover, and detailed information about "Hierarchical Feature Selection for Knowledge Discovery", which was released on 29 November 2018. We suggest perusing the summary before initiating your download. This book is a top selection for enthusiasts of the Computers genre.

Summary of Hierarchical Feature Selection for Knowledge Discovery by Cen Wan PDF

This book is the first work that systematically describes the procedure of data mining and knowledge discovery on Bioinformatics databases by using the state-of-the-art hierarchical feature selection algorithms. The novelties of this book are three-fold. To begin with, this book discusses the hierarchical feature selection in depth, which is generally a novel research area in Data Mining/Machine Learning. Seven different state-of-the-art hierarchical feature selection algorithms are discussed and evaluated by working with four types of interpretable classification algorithms (i.e. three types of Bayesian network classification algorithms and the k-nearest neighbours classification algorithm). Moreover, this book discusses the application of those hierarchical feature selection algorithms on the well-known Gene Ontology database, where the entries (terms) are hierarchically structured. Gene Ontology database that unifies the representations of gene and gene products annotation provides the resource for mining valuable knowledge about certain biological research topics, such as the Biology of Ageing. Furthermore, this book discusses the mined biological patterns by the hierarchical feature selection algorithms relevant to the ageing-associated genes. Those patterns reveal the potential ageing-associated factors that inspire future research directions for the Biology of Ageing research.


Detail About Hierarchical Feature Selection for Knowledge Discovery PDF

  • Author : Cen Wan
  • Publisher : Springer
  • Genre : Computers
  • Total Pages : 120 pages
  • ISBN : 3319979191
  • PDF File Size : 35,9 Mb
  • Language : English
  • Rating : 4/5 from 21 reviews

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Hierarchical Feature Selection for Knowledge Discovery

Hierarchical Feature Selection for Knowledge Discovery
  • Publisher : Springer
  • File Size : 30,7 Mb
  • Release Date : 29 November 2018
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This book is the first work that systematically describes the procedure of data mining and knowledge discovery on Bioinformatics databases by using the state-of-the-art hierarchical feature selection algorithms. The novelties

Feature Selection for Knowledge Discovery and Data Mining

Feature Selection for Knowledge Discovery and Data Mining
  • Publisher : Springer Science & Business Media
  • File Size : 48,6 Mb
  • Release Date : 06 December 2012
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As computer power grows and data collection technologies advance, a plethora of data is generated in almost every field where computers are used. The com puter generated data should be

Computational Methods of Feature Selection

Computational Methods of Feature Selection
  • Publisher : CRC Press
  • File Size : 35,9 Mb
  • Release Date : 29 October 2007
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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

Data Science Concepts and Techniques with Applications

Data Science Concepts and Techniques with Applications
  • Publisher : Springer Nature
  • File Size : 24,5 Mb
  • Release Date : 02 April 2023
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This textbook comprehensively covers both fundamental and advanced topics related to data science. Data science is an umbrella term that encompasses data analytics, data mining, machine learning, and several other

Advances in Knowledge Discovery and Management

Advances in Knowledge Discovery and Management
  • Publisher : Springer
  • File Size : 29,5 Mb
  • Release Date : 25 October 2013
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This book is a collection of representative and novel works done in Data Mining, Knowledge Discovery, Clustering and Classification that were originally presented in French at the EGC'2012 Conference held

Feature Selection for Knowledge Discovery and Data Mining

Feature Selection for Knowledge Discovery and Data Mining
  • Publisher : LAP Lambert Academic Publishing
  • File Size : 50,6 Mb
  • Release Date : 13 June 2024
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With advanced computer technologies and their omnipresent usage, data accumulates in a speed unmatchable by the human's capacity to process data. To meet this growing challenge, the research community of

Large Scale Hierarchical Classification: State of the Art

Large Scale Hierarchical Classification: State of the Art
  • Publisher : Springer
  • File Size : 41,6 Mb
  • Release Date : 09 October 2018
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This SpringerBrief covers the technical material related to large scale hierarchical classification (LSHC). HC is an important machine learning problem that has been researched and explored extensively in the past

Metaheuristics in Machine Learning: Theory and Applications

Metaheuristics in Machine Learning: Theory and Applications
  • Publisher : Springer Nature
  • File Size : 22,9 Mb
  • Release Date : 13 June 2024
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This book is a collection of the most recent approaches that combine metaheuristics and machine learning. Some of the methods considered in this book are evolutionary, swarm, machine learning, and

Spectral Feature Selection for Data Mining (Open Access)

Spectral Feature Selection for Data Mining (Open Access)
  • Publisher : CRC Press
  • File Size : 53,5 Mb
  • Release Date : 14 December 2011
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Spectral Feature Selection for Data Mining introduces a novel feature selection technique that establishes a general platform for studying existing feature selection algorithms and developing new algorithms for emerging problems