Feature selection and classification of leukocytes using random forest.
In automatic segmentation of leukocytes from the complex morphological background of tissue section images, a vast number of artifacts/noise are also extracted causing large amount of multivariate data generation. This multivariate data degrades the performance of a classifier to discriminate betwee...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 52; no. 12; pp. 1041 - 1053 |
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| Autores principales: | , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Dec2014
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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=103853461&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 103853461 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Dec2014 vid: 52 iid: 12 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 103853461 NLM25284218 2012794285 10.1007/s11517-014-1200-8 NLM25284218 103853461 ppf: 1041 ppct: 12 formats: fmt: @attributes: type: P tig: atl: Feature selection and classification of leukocytes using random forest. aug: au: Saraswat, Mukesh Arya, K V affil: ABV-Indian Institute of Information Technology and Management, Gwalior, 474010, India, saraswatmukesh@gmail.com. sug: subj: Decision Trees Image Processing, Computer Assisted Methods Information Science Methods Leukocytes Leukocytes Classification Algorithms Animal Studies Mice Models, Biological Skin ab: In automatic segmentation of leukocytes from the complex morphological background of tissue section images, a vast number of artifacts/noise are also extracted causing large amount of multivariate data generation. This multivariate data degrades the performance of a classifier to discriminate between leukocytes and artifacts/noise. However, the selection of prominent features plays an important role in reducing the computational complexity and increasing the performance of the classifier as compared to a high-dimensional features space. Therefore, this paper introduces a novel Gini importance-based binary random forest feature selection method. Moreover, the random forest classifier is used to classify the extracted objects into artifacts, mononuclear cells, and polymorphonuclear cells. The experimental results establish that the proposed method effectively eliminates the irrelevant features, maintaining the high classification accuracy as compared to other feature reduction methods. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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