• Comparison of Decision Trees U...

Treffer: Comparison of Decision Trees Used in Data Mining = Veri madenciliginde kullanilan karar agaçlarinin karsilastirilmasi

Title:
Comparison of Decision Trees Used in Data Mining = Veri madenciliginde kullanilan karar agaçlarinin karsilastirilmasi
Language:
English
Turkish
Authors:
Aksu, Gökhan (ORCID 0000-0003-2563-6112), Dogan, Nuri (ORCID 0000-0001-6274-2016)
Source:
Pegem Journal of Education and Instruction. 2019 9(4):1183-1208.
Availability:
Pegem Academy Publishing and Educational Guidance Services TLC. Mesrutiyet Caddesi, No: 45, Ankara, Kizilay 06420, Turkey. e-mail: editor@pegegog.net; Web site: http://www.pegegog.net/
Peer Reviewed:
Y
Page Count:
27
Publication Date:
2019
Document Type:
Fachzeitschrift Journal Articles<br />Multilingual/Bilingual Materials<br />Reports - Research
Education Level:
Higher Education
Postsecondary Education
Secondary Education
Descriptors:
Data Analysis, Decision Support Systems, Visual Aids, College Students, Classification, Scientific Literacy, Foreign Countries, Achievement Tests, Secondary School Students, International Assessment, Mathematics
Geographic Terms:
Turkey
Assessment and Survey Identifiers:
Program for International Student Assessment
ISSN:
2146-0655
Entry Date:
2019
Accession Number:
EJ1231983
Database:
ERIC

Weitere Informationen

The purpose of this study is to compare decision trees obtained by data mining algorithms used in various areas in recent years according to different criteria. In the study, similar and different aspects of the decision trees obtained by different methods for classifying the students as successful and unsuccessful in terms of science literacy were revealed with the help of 12 independent variables included in the PISA 2015 student survey. Data collected across Turkey, from a total of 5895 students in the age group of 15, were analyzed in Java-based Weka software, which has an open source code. As a result of the analysis, it was found that the most successful algorithms in terms of correct classification rate were respectively Logistic Model, Hoeffding Tree, J.48, REPTree and Random Tree. In addition, regarding the decision trees obtained by different learning algorithms, variables that have been effective in the classification were found to be different. According to the results, it was concluded that independent variables found to be effective in the classification of the students for the decision trees obtained by different algorithms differed from each other and it was suggested to report the finding of more than one algorithm instead of those of only one algorithm.

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