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Feature Selection in Gene Expression Data Using Principal Component Analysis and Rough Set Theory | SpringerLink
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In many fields such as data mining, machine learning, pattern recognition and signal processing, data sets containing huge number of features are often involved. Feature selection is an essential data preprocessing technique for such high-dimensional data...
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Keywords {🔍}
data, chapter, feature, information, principal, rough, google, scholar, analysis, selection, set, component, privacy, cookies, content, journal, publish, gene, expression, classification, reduction, computer, springer, research, search, systems, mishra, learning, features, applied, science, download, usd, personal, log, find, software, tools, algorithms, biological, theory, debahuti, book, mining, machine, sets, dimensionality, approach, method, discover,
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ScholarlyArticle:
headline:Feature Selection in Gene Expression Data Using Principal Component Analysis and Rough Set Theory
pageEnd:100
pageStart:91
image:https://media.springernature.com/w153/springer-static/cover/book/978-1-4419-7046-6.jpg
genre:
Biomedical and Life Sciences
Biomedical and Life Sciences (R0)
isPartOf:
name:Software Tools and Algorithms for Biological Systems
isbn:
978-1-4419-7046-6
978-1-4419-7045-9
type:Book
publisher:
name:Springer New York
logo:
url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
type:ImageObject
type:Organization
author:
name:Debahuti Mishra
affiliation:
name:Siksha O Anusandhan University
address:
name:Department of Computer Science & Engineering, Institute of Technical Education & Research, Siksha O Anusandhan University, Bhubaneswar, India
type:PostalAddress
type:Organization
email:[email protected]
type:Person
name:Rajashree Dash
affiliation:
type:Person
name:Amiya Kumar Rath
affiliation:
type:Person
name:Milu Acharya
affiliation:
type:Person
keywords:Data preprocessing, Feature selection, Principal component analysis, Rough sets, Lower approximation, Upper approximation
description:In many fields such as data mining, machine learning, pattern recognition and signal processing, data sets containing huge number of features are often involved. Feature selection is an essential data preprocessing technique for such high-dimensional data classification tasks. Traditional dimensionality reduction approach falls into two categories: Feature Extraction (FE) and Feature Selection (FS). Principal component analysis is an unsupervised linear FE method for projecting high-dimensional data into a low-dimensional space with minimum loss of information. It discovers the directions of maximal variances in the data. The Rough set approach to feature selection is used to discover the data dependencies and reduction in the number of attributes contained in a data set using the data alone, requiring no additional information. For selecting discriminative features from principal components, the Rough set theory can be applied jointly with PCA, which guarantees that the selected principal components will be the most adequate for classification. We call this method Rough PCA. The proposed method is successfully applied for choosing the principal features and then applying the Upper and Lower Approximations to find the reduced set of features from a gene expression data.
datePublished:2011
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Book:
name:Software Tools and Algorithms for Biological Systems
isbn:
978-1-4419-7046-6
978-1-4419-7045-9
Organization:
name:Springer New York
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name:Siksha O Anusandhan University
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name:Debahuti Mishra
affiliation:
name:Siksha O Anusandhan University
address:
name:Department of Computer Science & Engineering, Institute of Technical Education & Research, Siksha O Anusandhan University, Bhubaneswar, India
type:PostalAddress
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email:[email protected]
name:Rajashree Dash
affiliation:
name:Amiya Kumar Rath
affiliation:
name:Milu Acharya
affiliation:
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name:Department of Computer Science & Engineering, Institute of Technical Education & Research, Siksha O Anusandhan University, Bhubaneswar, India
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