Regularized F-measure maximization for feature selection and classification.
Receiver Operating Characteristic (ROC) analysis is a common tool for assessing the performance of various classifications. It gained much popularity in medical and other fields including biological markers and, diagnostic test. This is particularly due to the fact that in real-world problems miscla...
| Publicado en: | Journal of Biomedicine & Biotechnology pp. 8p - 9 |
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| Autores principales: | , , |
| Formato: | equations & formulas tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
2009 Regular Issue
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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=105438724&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105438724 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 11107243 137K jtl: Journal of Biomedicine & Biotechnology issn: 11107243 maglogo: N pubinfo: dt: 2009 Regular Issue pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 105438724 2010398025 10.1155/2009/617946 NLM19421401 105438724 ppf: 8p ppct: 1 formats: fmt: @attributes: type: P tig: atl: Regularized F-measure maximization for feature selection and classification. aug: au: Liu Z Tan M Jiang F affil: Division of Biostatistics, University of Maryland Greenebaum Cancer Center, Baltimore, MD 21201, USA. zliu@umm.edu sug: subj: Algorithms Mathematics Statistics Costs and Cost Analysis Classification Methylation ROC Curve ab: Receiver Operating Characteristic (ROC) analysis is a common tool for assessing the performance of various classifications. It gained much popularity in medical and other fields including biological markers and, diagnostic test. This is particularly due to the fact that in real-world problems misclassification costs are not known, and thus, ROC curve and related utility functions such as F-measure can be more meaningful performance measures. F-measure combines recall and precision into a global measure. In this paper, we propose a novel method through regularized F-measure maximization. The proposed method assigns different costs to positive and negative samples and does simultaneous feature selection and prediction with L(1) penalty. This method is useful especially when data set is highly unbalanced, or the labels for negative (positive) samples are missing. Our experiments with the benchmark, methylation, and high dimensional microarray data show that the performance of proposed algorithm is better or equivalent compared with the other popular classifiers in limited experiments. pubtype: Academic Journal doctype: equations & formulas tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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