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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  2/2018 - 4

Generic Approach for Interpretation of PCA Results - Use Case on Learner's Activity in Social Media Tools

MIHAESCU, M. C. See more information about MIHAESCU, M. C. on SCOPUS See more information about MIHAESCU, M. C. on IEEExplore See more information about MIHAESCU, M. C. on Web of Science, POPESCU, P. S. See more information about  POPESCU, P. S. on SCOPUS See more information about  POPESCU, P. S. on SCOPUS See more information about POPESCU, P. S. on Web of Science, MOCANU, M. L. See more information about MOCANU, M. L. on SCOPUS See more information about MOCANU, M. L. on SCOPUS See more information about MOCANU, M. L. on Web of Science
 
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Download PDF pdficon (1,304 KB) | Citation | Downloads: 408 | Views: 1,394

Author keywords
data engineering, knowledge representation, machine learning, social network services, social computing

References keywords
analysis(13), principal(11), component(10), learning(5), education(5), student(4), social(4), review(4), research(4), advanced(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2018-05-31
Volume 18, Issue 2, Year 2018, On page(s): 27 - 34
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2018.02004
Web of Science Accession Number: 000434245000004
SCOPUS ID: 85047847074

Abstract
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Intensive usage of social media tools for educational purposes transformed many previously tackled issues from classical e-Learning systems. Among the most general challenging issues reside in building classification models having the performed activities set as independent variables and final grade as dependent variable. A critical step in data analysis process regards building interpretable models in terms of explaining feature values and ranges along with their influence on target class. We asked whether dimensionality reduction techniques may be effectively used such that high quality interpretable models are obtained. Principal component analysis (PCA) dimensionality reduction technique, scaling and several classical classification techniques were used to create a data analysis pipeline that produces classification models with similar accuracy of initial classification models built on raw available data. Experimental results show that features that characterize the activity performed on each social tool and on all tools are highly interpretable in our classification context. The proposed approach is flexible and can be adapted to similar practical use cases in which a large number of features is difficult to be interpreted and a digest is required as being more useful for bringing a better insight on data.


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