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    DWFS: A Wrapper Feature Selection Tool Based on a Parallel Genetic Algorithm

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    Type
    Article
    Authors
    Soufan, Othman cc
    Kleftogiannis, Dimitrios A. cc
    Kalnis, Panos cc
    Bajic, Vladimir B. cc
    KAUST Department
    Computational Bioscience Research Center (CBRC)
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Computer Science Program
    Applied Mathematics and Computational Science Program
    Date
    2015-02-26
    Permanent link to this record
    http://hdl.handle.net/10754/346688
    
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    Abstract
    Many scientific problems can be formulated as classification tasks. Data that harbor relevant information are usually described by a large number of features. Frequently, many of these features are irrelevant for the class prediction. The efficient implementation of classification models requires identification of suitable combinations of features. The smaller number of features reduces the problem's dimensionality and may result in higher classification performance. We developed DWFS, a web-based tool that allows for efficient selection of features for a variety of problems. DWFS follows the wrapper paradigm and applies a search strategy based on Genetic Algorithms (GAs). A parallel GA implementation examines and evaluates simultaneously large number of candidate collections of features. DWFS also integrates various filteringmethods thatmay be applied as a pre-processing step in the feature selection process. Furthermore, weights and parameters in the fitness function of GA can be adjusted according to the application requirements. Experiments using heterogeneous datasets from different biomedical applications demonstrate that DWFS is fast and leads to a significant reduction of the number of features without sacrificing performance as compared to several widely used existing methods. DWFS can be accessed online at www.cbrc.kaust.edu.sa/dwfs.
    Citation
    DWFS: A Wrapper Feature Selection Tool Based on a Parallel Genetic Algorithm 2015, 10 (2):e0117988 PLOS ONE
    Publisher
    Public Library of Science (PLoS)
    Journal
    PLoS ONE
    DOI
    10.1371/journal.pone.0117988
    PubMed ID
    25719748
    PubMed Central ID
    PMC4342225
    Additional Links
    http://dx.plos.org/10.1371/journal.pone.0117988
    ae974a485f413a2113503eed53cd6c53
    10.1371/journal.pone.0117988
    Scopus Count
    Collections
    Articles; Applied Mathematics and Computational Science Program; Computer Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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