Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/9642
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dc.contributor.authorXie, J-
dc.contributor.authorLei, J-
dc.contributor.authorXie, W-
dc.contributor.authorShi, Y-
dc.contributor.authorLiu, X-
dc.date.accessioned2014-12-23T16:22:19Z-
dc.date.available2013-05-
dc.date.available2014-12-23T16:22:19Z-
dc.date.issued2013-
dc.identifier.citationHealth Information Science and Systems, 1: 10, 2013en_US
dc.identifier.issn2047-2501-
dc.identifier.urihttp://www.hissjournal.com/content/1/1/10-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/9642-
dc.description.abstractThis paper proposes two-stage hybrid feature selection algorithms to build the stable and efficient diagnostic models where a new accuracy measure is introduced to assess the models. The two-stage hybrid algorithms adopt Support Vector Machines (SVM) as a classification tool, and the extended Sequential Forward Search (SFS), Sequential Forward Floating Search (SFFS), and Sequential Backward Floating Search (SBFS), respectively, as search strategies, and the generalized F-score (GF) to evaluate the importance of each feature. The new accuracy measure is used as the criterion to evaluated the performance of a temporary SVM to direct the feature selection algorithms. These hybrid methods combine the advantages of filters and wrappers to select the optimal feature subset from the original feature set to build the stable and efficient classifiers. To get the stable, statistical and optimal classifiers, we conduct 10-fold cross validation experiments in the first stage; then we merge the 10 selected feature subsets of the 10-cross validation experiments, respectively, as the new full feature set to do feature selection in the second stage for each algorithm. We repeat the each hybrid feature selection algorithm in the second stage on the one fold that has got the best result in the first stage. Experimental results show that our proposed two-stage hybrid feature selection algorithms can construct efficient diagnostic models which have got better accuracy than that built by the corresponding hybrid feature selection algorithms without the second stage feature selection procedures. Furthermore our methods have got better classification accuracy when compared with the available algorithms for diagnosing erythemato-squamous diseases.en_US
dc.language.isoenen_US
dc.publisherBioMed Centralen_US
dc.subjectTwo-stage hybrid algorithmsen_US
dc.subjectSupport Vector Machines (SVM)en_US
dc.subjectSequential Forward Search (SFS)en_US
dc.subjectSequential Forward Floating Search (SFFS)en_US
dc.subjectSequential Backward Floating Search (SBFS)en_US
dc.subjectGeneralized F-score (GF)en_US
dc.titleTwo-stage hybrid feature selection algorithms for diagnosing erythemato-squamous diseasesen_US
dc.typeArticleen_US
dc.relation.isPartOfHealth Information Science and Systems-
dc.relation.isPartOfHealth Information Science and Systems-
pubs.organisational-data/Brunel-
pubs.organisational-data/Brunel/Brunel Staff by College/Department/Division-
pubs.organisational-data/Brunel/Brunel Staff by College/Department/Division/College of Engineering, Design and Physical Sciences-
pubs.organisational-data/Brunel/Brunel Staff by College/Department/Division/College of Engineering, Design and Physical Sciences/Dept of Computer Science-
pubs.organisational-data/Brunel/Brunel Staff by College/Department/Division/College of Engineering, Design and Physical Sciences/Dept of Computer Science/Computer Science-
pubs.organisational-data/Brunel/Brunel Staff by Institute/Theme-
pubs.organisational-data/Brunel/Brunel Staff by Institute/Theme/Institute of Environmental, Health and Societies-
pubs.organisational-data/Brunel/Brunel Staff by Institute/Theme/Institute of Environmental, Health and Societies/Synthetic Biology-
pubs.organisational-data/Brunel/University Research Centres and Groups-
pubs.organisational-data/Brunel/University Research Centres and Groups/Brunel Business School - URCs and Groups-
pubs.organisational-data/Brunel/University Research Centres and Groups/Brunel Business School - URCs and Groups/Centre for Research into Entrepreneurship, International Business and Innovation in Emerging Markets-
pubs.organisational-data/Brunel/University Research Centres and Groups/School of Health Sciences and Social Care - URCs and Groups-
pubs.organisational-data/Brunel/University Research Centres and Groups/School of Health Sciences and Social Care - URCs and Groups/Brunel Institute for Ageing Studies-
pubs.organisational-data/Brunel/University Research Centres and Groups/School of Health Sciences and Social Care - URCs and Groups/Brunel Institute of Cancer Genetics and Pharmacogenomics-
pubs.organisational-data/Brunel/University Research Centres and Groups/School of Health Sciences and Social Care - URCs and Groups/Centre for Systems and Synthetic Biology-
pubs.organisational-data/Brunel/University Research Centres and Groups/School of Information Systems, Computing and Mathematics - URCs and Groups-
pubs.organisational-data/Brunel/University Research Centres and Groups/School of Information Systems, Computing and Mathematics - URCs and Groups/Multidisclipary Assessment of Technology Centre for Healthcare (MATCH)-
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