A Procedure for Extending Input Selection Algorithms to Low Quality Data in Modelling Problems with Application to the Automatic Grading of Uploaded Assignments
Joint Authors
Sanchez, L.
Otero, José
Palacios, Ana
Suárez, Rosario
Junco, Luis
Couso, Inés
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-07-07
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Otero, José& Palacios, Ana& Suárez, Rosario& Junco, Luis& Couso, Inés& Sanchez, L.. 2014. A Procedure for Extending Input Selection Algorithms to Low Quality Data in Modelling Problems with Application to the Automatic Grading of Uploaded Assignments. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-11.
https://search.emarefa.net/detail/BIM-1049731
Modern Language Association (MLA)
Otero, José…[et al.]. A Procedure for Extending Input Selection Algorithms to Low Quality Data in Modelling Problems with Application to the Automatic Grading of Uploaded Assignments. The Scientific World Journal No. 2014 (2014), pp.1-11.
https://search.emarefa.net/detail/BIM-1049731
American Medical Association (AMA)
Otero, José& Palacios, Ana& Suárez, Rosario& Junco, Luis& Couso, Inés& Sanchez, L.. A Procedure for Extending Input Selection Algorithms to Low Quality Data in Modelling Problems with Application to the Automatic Grading of Uploaded Assignments. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-11.
https://search.emarefa.net/detail/BIM-1049731
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1049731