A Bio-inspired Algorithm based Multi-class Classification Scheme for Microarray Gene Data.
Microarray gene data is widely known for its high dimensionality and volume. The utilization of microarray gene data is increasing now-a-days, owing to the advancement of medical science. Microarray gene data helps in diagnosing diseases quite accurately. However, processing microarray gene data is...
| Publicado en: | Journal of Medical Systems Vol. 43; no. 7 |
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| Autores principales: | , |
| Formato: | algorithm equations & formulas tables/charts Journal Article |
| Publicado: |
Springer Nature
Jul2019
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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=137182957&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 137182957 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Jul2019 vid: 43 iid: 7 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 137182957 137182957 137182957 10.1007/s10916-019-1353-y 137182957 ppct: 1 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Bio-inspired Algorithm based Multi-class Classification Scheme for Microarray Gene Data. aug: au: Scaria, LT. Thomas Christopher, T. affil: Department of Computer Science, St. Pius X College, Kasaragod, Kerala, India sug: subj: Algorithms Microarray Analysis Gene Expression ab: Microarray gene data is widely known for its high dimensionality and volume. The utilization of microarray gene data is increasing now-a-days, owing to the advancement of medical science. Microarray gene data helps in diagnosing diseases quite accurately. However, processing microarray gene data is difficult and is usually not understandable. Taking this challenge into account, this work presents a user-friendly rule based classification model, which is easily understandable and does not demand users to have prior knowledge. The classification rules are formed with the help of cuckoo search optimization algorithm and the rules are pruned by the associative rule mining. Finally, the classification is performed with the help of the pruned rules. The performance of the proposed approach is satisfactory in terms of accuracy, sensitivity, specificity and time consumption. pubtype: Academic Journal doctype: algorithm equations & formulas tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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