Comparison of LDA and SPRT on Clinical Dataset Classifications.
In this work, we investigate the well-known classification algorithm LDA as well as its close relative SPRT. SPRT affords many theoretical advantages over LDA. It allows specification of desired classification error rates á and â and is expected to be faster in predicting the class label of a new in...
| Publicado en: | Biomedical Informatics Insights Vol. 4; pp. 1 - 8 |
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| Autores principales: | , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2011
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104515364&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104515364 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 11782226 B077 jtl: Biomedical Informatics Insights issn: 11782226 maglogo: Y pubinfo: dt: 2011 vid: 4 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 104515364 71531653 10.4137/BII.S6935 104515364 ppf: 1 ppct: 7 formats: fmt: @attributes: type: P tig: atl: Comparison of LDA and SPRT on Clinical Dataset Classifications. aug: au: Chih Lee Nkounkou, Brittany Chun-Hsi Huang affil: Computer Science and Engineering Department, University of Connecticut, Storrs, CT 06269, USA sug: subj: Gene Expression Algorithms Classification Statistics Human Funding Source Colonic Neoplasms Parkinson Disease ab: In this work, we investigate the well-known classification algorithm LDA as well as its close relative SPRT. SPRT affords many theoretical advantages over LDA. It allows specification of desired classification error rates á and â and is expected to be faster in predicting the class label of a new instance. However, SPRT is not as widely used as LDA in the pattern recognition and machine learning community. For this reason, we investigate LDA, SPRT and a modified SPRT (MSPRT) empirically using clinical datasets from Parkinson's disease, colon cancer, and breast cancer. We assume the same normality assumption as LDA and propose variants of the two SPRT algorithms based on the order in which the components of an instance are sampled. Leave-one-out cross-validation is used to assess and compare the performance of the methods. The results indicate that two variants, SPRT-ordered and MSPRT-ordered, are superior to LDA in terms of prediction accuracy. Moreover, on average SPRT-ordered and MSPRT-ordered examine less components than LDA before arriving at a decision. These advantages imply that SPRT-ordered and MSPRT-ordered are the preferred algorithms over LDA when the normality assumption can be justified for a dataset. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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