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Title:
Feature Selection in Gene Expression Data Using Principal Component Analysis and Rough Set Theory | SpringerLink
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...
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Keywords {🔍}

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Topics {✒️}

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Schema {🗺️}

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
      isAccessibleForFree:
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         type:WebPageElement
      context:https://schema.org
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
      logo:
         url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
         type:ImageObject
      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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      url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
Person:
      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]
      name:Rajashree Dash
      affiliation:
      name:Amiya Kumar Rath
      affiliation:
      name:Milu Acharya
      affiliation:
PostalAddress:
      name:Department of Computer Science & Engineering, Institute of Technical Education & Research, Siksha O Anusandhan University, Bhubaneswar, India
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