Comparison of Classification Performances of MARS and BRT Data Mining Methods: ABİDE- 2016 Case

Şevgin, Hikmet; Önen, Emine

doi:10.15390/eb.2022.10575

Cited by 3 publications

(2 citation statements)

References 28 publications

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“…AUC: AUC, also known as the Area Under the Receiver Operating Characteristic (ROC) Curve, serves as a quantitative indicator of the model's accuracy. The magnitude of this area is a demonstrative measure of the classification model's performance efficacy [44].…”

Section: Performance Criteriamentioning

confidence: 99%

Classification of Liver Disorders Diagnosis using Naïve Bayes Method

Bezek Güre

2024

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi

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Liver diseases pose a significant health challenge, necessitating robust predictive tools for early diagnosis. This study aims to determine the predictive performance of Naive Bayes classifier, one of the data mining algorithms, in the classification of liver diseases. The study applied 5, 10 and 20-fold cross-validation method. Trying to determine the effect of the cross-validation (CV) method used on the classification performance, this study used the "BUPA" dataset in the UCI Machine Learning Repository database for this purpose. The dataset consists of 6 variables and 345 examples. Orange program was used for data analysis. The study showed that the accuracy of the Naive bayes method were 64.6%, 66.7% and 64.3%, respectively. Accordingly, it can be said that the 10-fold CV method performs better. Compared to similar studies, it can be claimed that the analysis results obtained with the Orange program are better.

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Section: Performance Criteriamentioning

confidence: 99%

Classification of Liver Disorders Diagnosis using Naïve Bayes Method

Bezek Güre

2024

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi

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“…The emergence and development of ensemble learning in the last quarter can be regarded as a reflection of this process. On account of the combination of basic statistical methods to generate ensemble learning methods, the results with high classification success and precise prediction as well as low error variance have been obtained (Bauer & Kohavi, 1999;Hansen & Salamon, 1990;Onan, 2015;Opitz & Shavlik, 1996;Polikar, 2006;Sagi & Rokach, 2018) and, in this respect, its use has recently increased in various areas such as health, economy, banking, agriculture, engineering, business and education (Akman, 2010;Şevgin & Önen 2022). There have been several studies employing ensemble methods encountered in the literature (Abidi et al, 2020;Baskin et al, 2017a;Baskin et al, 2017b;Dietterich, 2000;Dietterich, 2002;Freund & Schapire, 1996;Friedman, 2001;Kapucu & Cubukcu, 2021;Kausar et al, 2020;Li et al, 2022;Mousavi & Eftekhari, 2015;Pong-Inwong & Kaewmak, 2016;Steinki & Mohammad, 2015;Wang et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

A comparative study of ensemble methods in the field of education: Bagging and Boosting algorithms

ŞEVGİN

2023

International Journal of Assessment Tools in Education

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This study aims to conduct a comparative study of Bagging and Boosting algorithms among ensemble methods and to compare the classification performance of TreeNet and Random Forest methods using these algorithms on the data extracted from ABİDE application in education. The main factor in choosing them for analyses is that they are Ensemble methods combining decision trees via Bagging and Boosting algorithms and creating a single outcome by combining the outputs obtained from each of them. The data set consists of mathematics scores of ABİDE (Academic Skills Monitoring and Evaluation) 2016 implementation and various demographic variables regarding students. The study group involves 5000 students randomly recruited. On the deletion of loss data and assignment procedures, this number decreased to 4568. The analyses showed that the TreeNet method performed more successfully in terms of classification accuracy, sensitivity, F1-score and AUC value based on sample size, and the Random Forest method on specificity and accuracy. It can be alleged that the TreeNet method is more successful in all numerical estimation error rates for each sample size by producing lower values compared to the Random Forest method. When comparing both analysis methods based on ABİDE data, considering all the conditions, including sample size, cross validity and performance criteria following the analyses, TreeNet can be said to exhibit higher classification performance than Random Forest. Unlike a single classifier or predictive method, the classification or prediction of multiple methods by using Boosting and Bagging algorithms is considered important for the results obtained in education.

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Reviewing the Factors Affecting PISA Reading Skills by Using Random Forest and MARS Methods

Güre

Şevgin

KAYRİ

2023

International Journal of Contemporary Educational Research

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The research aims to determine the factors affecting PISA 2018 reading skills using Random Forest and MARS methods and to compare their prediction abilities. This study used the information from 5713 students, 2838 (49.7%) male and 2875 (50.3%) female in the PISA 2018 Turkey. The analysis shows the MARS method performed better than the Random Forest method. The most significant factor affecting reading skills in Turkey is “the number of books in the house” in both methods. The variables the MARS method finds significant are “students' perception of difficulty, motivation for reading skills, father’s educational status, reading pleasure, bullying experience of the student, mother's educational status, attitude towards school, classical artifacts at home, supplementary school books at home, competition at school, competitive power, cooperation perception at school, reading frequency, self-efficacy, poetry books at home, anxiety about reading skills and teacher support.” However, the other variables had no relation to prediction. This study is expected to serve as an example of data mining application in educational research

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Comparison of Classification Performances of MARS and BRT Data Mining Methods: ABİDE- 2016 Case

Cited by 3 publications

References 28 publications

Classification of Liver Disorders Diagnosis using Naïve Bayes Method

Classification of Liver Disorders Diagnosis using Naïve Bayes Method

A comparative study of ensemble methods in the field of education: Bagging and Boosting algorithms

Reviewing the Factors Affecting PISA Reading Skills by Using Random Forest and MARS Methods

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