A procedure for extending input selection algorithms to low quality data in modelling problems with application to the automatic grading of uploaded assignments.
When selecting relevant inputs in modeling problems with low quality data, the ranking of the most informative inputs is also uncertain. In this paper, this issue is addressed through a new procedure that allows the extending of different crisp feature selection algorithms to vague data. The partial...
| Publicado en: | Scientific World Journal pp. 468405 - 468406 |
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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=103839848&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 103839848 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: 103839848 NLM25114967 2012682938 10.1155/2014/468405 NLM25114967 PMC4119680 103839848 ppf: 468405 ppct: 1 formats: tig: atl: A procedure for extending input selection algorithms to low quality data in modelling problems with application to the automatic grading of uploaded assignments. aug: au: Otero, José Palacios, Ana Suárez, Rosario Junco, Luis Couso, Inés Sánchez, Luciano affil: Computer Science Department, Universidad de Oviedo, Sedes Departamentales, Edificio 1, Campus de Viesques, 33203 Gijón, Spain. sug: subj: Models, Theoretical Algorithms Human ab: When selecting relevant inputs in modeling problems with low quality data, the ranking of the most informative inputs is also uncertain. In this paper, this issue is addressed through a new procedure that allows the extending of different crisp feature selection algorithms to vague data. The partial knowledge about the ordinal of each feature is modelled by means of a possibility distribution, and a ranking is hereby applied to sort these distributions. It will be shown that this technique makes the most use of the available information in some vague datasets. The approach is demonstrated in a real-world application. In the context of massive online computer science courses, methods are sought for automatically providing the student with a qualification through code metrics. Feature selection methods are used to find the metrics involved in the most meaningful predictions. In this study, 800 source code files, collected and revised by the authors in classroom Computer Science lectures taught between 2013 and 2014, are analyzed with the proposed technique, and the most relevant metrics for the automatic grading task are discussed. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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