Classification based on pruning and double covered rule sets for the internet of things applications.
The Internet of things (IOT) is a hot issue in recent years. It accumulates large amounts of data by IOT users, which is a great challenge to mining useful knowledge from IOT. Classification is an effective strategy which can predict the need of users in IOT. However, many traditional rule-based cla...
| Publicado en: | Scientific World Journal pp. 984375 - 984376 |
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| Autores principales: | , , |
| Formato: | research Journal Article |
| Publicado: |
Wiley-Blackwell
2014
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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=104021601&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104021601 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1537744X 1BX5 jtl: Scientific World Journal issn: 1537744X maglogo: N pubinfo: dt: 2014 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 104021601 104021601 NLM24511304 2012470725 10.1155/2014/984375 NLM24511304 PMC3913369 104021601 ppf: 984375 ppct: 1 formats: tig: atl: Classification based on pruning and double covered rule sets for the internet of things applications. aug: au: Li, Shasha Zhou, Zhongmei Wang, Weiping affil: Department of Computer Science and Engineering, Minnan Normal University, Zhangzhou 363000, China. sug: subj: Algorithms Internet Models, Theoretical ab: The Internet of things (IOT) is a hot issue in recent years. It accumulates large amounts of data by IOT users, which is a great challenge to mining useful knowledge from IOT. Classification is an effective strategy which can predict the need of users in IOT. However, many traditional rule-based classifiers cannot guarantee that all instances can be covered by at least two classification rules. Thus, these algorithms cannot achieve high accuracy in some datasets. In this paper, we propose a new rule-based classification, CDCR-P (Classification based on the Pruning and Double Covered Rule sets). CDCR-P can induce two different rule sets A and B. Every instance in training set can be covered by at least one rule not only in rule set A, but also in rule set B. In order to improve the quality of rule set B, we take measure to prune the length of rules in rule set B. Our experimental results indicate that, CDCR-P not only is feasible, but also it can achieve high accuracy. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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