Improving the Accuracy of Feature Selection in Big Data Mining Using Accelerated Flower Pollination (AFP) Algorithm.
In recent times, the main problem associated with big data analytics is its high dimensional data over the search space. Such data gathers continuously in search space making traditional algorithms infeasible for data mining in real time environment. Hence, feature selection is an important method t...
| Publicado en: | Journal of Medical Systems Vol. 43; no. 4 |
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| Autores principales: | , , |
| Formato: | algorithm equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2019
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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=135714988&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 135714988 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Apr2019 vid: 43 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 135714988 135714988 135714988 10.1007/s10916-019-1200-1 135714988 ppct: 1 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Improving the Accuracy of Feature Selection in Big Data Mining Using Accelerated Flower Pollination (AFP) Algorithm. aug: au: Venkatasalam, K. Rajendran, P. Thangavel, M. affil: Department of Computer Science & Engineering, Mahendra Engineering College, 637503, Namakkal, India sug: subj: Data Analytics Data Mining Algorithms Data Analysis Models, Statistical Time Factors ab: In recent times, the main problem associated with big data analytics is its high dimensional data over the search space. Such data gathers continuously in search space making traditional algorithms infeasible for data mining in real time environment. Hence, feature selection is an important method to lighten the load during processing while inducing a model for mining. However, mining over such high dimensional data leads to formulation of optimal feature subset, which grows exponentially and leads to intractable computational demand. In this paper, a novel lightweight mechanism is used as a feature selection method, which solves the after effects arising with optimal feature selection. The feature selection in big data mining is done using accelerated flower pollination (AFP) algorithm. This method improves the accuracy of feature selection with reduced processing time. The proposed method is tested under larger set of data with high dimensionality to test the performance of proposed method. pubtype: Academic Journal doctype: algorithm equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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