Classification of the placement success in the undergraduate placement examination according to decision trees with bagging and boosting methods

Okut, Hayrettin; TUĞ KAROĞLU, TUĞBA

doi:10.17776/csj.544639

Classification of the placement success in the undergraduate placement examination according to decision trees with bagging and boosting methods

Tuğba TUĞ KAROĞLU, (Milli Eğitim Bakanlığı, İpekyolu İMKB Fen Lisesi, Van, Türkiye)

Hayrettin OKUT (School of Medicine, Kansas University, Kansas, Amerika)

Cumhuriyet Science Journal

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Yıl: 2020 Cilt: 41 Sayı: 1 Sayfa Aralığı: 93 - 105 Metin Dili: İngilizce DOI: 10.17776/csj.544639 İndeks Tarihi: 26-10-2021

Classification of the placement success in the undergraduate placement examination according to decision trees with bagging and boosting methods

Öz:

The purpose of this study is to classify the data set which is created by taking students whoplaced to universities from 81 provinces, in accordance with Undergraduate PlacementExamination between the years 2010-2013 in Turkey, with Bagging and Boosting methodswhich are Ensemble algorithms. The data set which is used in the study was taken from thearchives of Turk-Stat. (Turkish Statistical Institute) and OSYM (Assessment, Selection andPlacement Center) and MATLAB statistical software program was used. In order to evaluateBagging and Boosting classification performances better, the success rates of the studentswere grouped into two groups. According to this, the provinces that were above the averagewere coded as 1, and the provinces below the average were coded as 0 and dependentvariables were created. The Bagging and Boosting ensemble algorithms were runaccordingly. In order to evaluate the prediction abilities of the Bagging and Boostingalgorithms, the data set was divided into training and testing. For this purpose, while the databetween 2010-2012 yearrs were used as training data, the data of the year 2013 were used astesting data. Accuracy, precision, recall and f-measure were used to demonstrate theperformance of the methods in the study. As a result, the performance in consequence of"Bagging” and “Boosting” methods were compared. According to this; it was determinedthat in all performance measure marginally “Boosting” method produced better results thanthe “Bagging” method.

Anahtar Kelime:

Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık

[1] Koyuncugil, A. S., Özgülbaş, N., İMKB'de İşlem Gören KOBİ'lerin güçlü ve zayıf Yönleri : Bir CHAID Karar Ağacı uygulaması. Dokuz Eylül Üniversitesi İİBF Dergisi. 23(1) (2008) 1-22.
[2] Hand, D.,Manilla, H., Smyth, P., Principles of Data Mining. MIT, USA, (2001) 546
[3] Augusty, S. M.,Izudheen, S., EnsembleClassifiers A Survey: Evaluation of Ensemble classifiers and data level methods to deal withim balanced data problem in protein- protein interactions. Review of Bionformatics and Biometrics, 2 (1) (2013) 1-9.
[4] Lee, S. L.A., Kouzani, A. Z., Hu, E. J., Random forest based lung nodule classification aided biclustering. Computerized Medical Imaging and Graphics,34 (2010) 535-542.
[5] Tartar, A., Kılıç, N., Akan, A., Bagging support vector machine approaches for pulmonary nodule detection. IEEE International Conference on Control, Decision and Information Technologies.Tunisia, (2013) 047-050.
[6] Zeng, X. D.,Chao, S., Wang, F., 2010. Optimization of Bagging Classifiers Based on SBCB Algorithm. Proceedings of the ninth International Conference on Machine Learning and Cybernetics.11-14 July (2010) Qingdao. 262- 267.
[7] Biggio, B.,Corona, I., Fumera, G., Giacinto, G., Roli, F., Bagging Classifiers for Fighting Poisoning Attacks in Adversarial Classification Tasks. Springer Verlag Berlin Heidelberg, (2011) 350-359.
[8] Breiman, L., Using iterated bagging to debias regressions. Machine Learnings, 45(3) (2001) 261- 277.
[9] Banfield, R. E.,Hall, L. O., Bowyer, K. W., Kegelmeyer, W. P., Ensemble diversity measures and their application to thinning. Information Fusion, 6(1) (2005) 49–62.
[10] Alfaro, E.,Gamez, M., Garcia, N., Adabag: An R package for classification with Boosting and Bagging. Journal of Statistical Software, 54(2) (2013) 1-35.
[11] Kumari, G. T., A Study of Bagging and Boosting approaches to develop meta- classifier. Engineering Science and Technology: An International Journal (ESTIJ), 2(5) (2012) 850- 855.
[12] Anonim, Öğrenci Seçme ve Yerleştirme Sistemi Yükseköğretim Programları ve Kontenjanları Kılavuzu.http://www.osym.gov.tr. (2013)
[13] [Zhou, Z. H., Ensemble Methods: Foundations and Algorithms.Chapman & Hall/CRC Machine Learning &Pattern Recognition Series. Boca Raton, FL, United States of America. (2012) 236.
[14] Zhang, C.,Ma, Y., Ensemble Learning, Chap. 1. Ensemble Machine Learning(Editor: R. Polikar). (2012) 1-17.
[15] Coşgun, E.,Limdi, N.A., Duarte C.W., High dimensional pharma cogenetic prediction of a continuous trait using machine learning techniques with application to warfar indose prediction in African American. Bioinformatics, 27(10) (2011) 1384-1389.
[16] Breiman, L., Bagging predictors. Machine Leraning, 24 (2) (1996) 123-140.
[17] Efron, B.,Tibshirani, R., An Introduction to the Bootstrap.Chapman and Hall. London. (1993) 430.
[18] Grubinger, T.,Kobel, C., Pfeiffer, K.P., Regression tree construction by bootstrap: Model search for DRG-systems applied to Austrian health-data. BMC Medical Informatics and Decision Making, 10 (9) (2010) 1-11.
[19] Song, M.,Breneman, C.M., Bi, J., Sukumar, N., Bennett, K.P., Cramer, S.M., Prediction of protein retention times in anion exchange chromatograph ysystems using support vector regression. Journal of Chemical Information and Computer Sciences, 42(6) (2002) 1347-1357.
[20] Prasad, A.M., Iverson, L.R., Liaw, A., Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems, 9 (2006) 181–199.
[21] Schapire, R. E., The strength of weak learnability. Machine Learning, 5 (2) (1990) 197–227.
[22] Schapire, R. E.,Freund, Y., Boosting: Foundations and Algorithms. MIT Press, Cambridge, London, England. (2012) 528.
[23] Elith, J.,Leathwick, J.R, Hastie, T., A working guide to boosted regression trees. Journal of Animal Ecology, 77(4) (2008) 802-813.
[24] Grove, A.J.,Schuurmans, D., Boosting in the Limit: Maximizing the Margin of Learned Ensembles. In: Proceeding of the AAAI-98. John Wiley&Sons Ltd, (1998)692-699.
[25] Ratsch, G.,Onoda, T., Müller, K. R., Soft Margins for AdaBoost. Machine Learning, 42 (3) (2001) 287-320.
[26] Bühlmann, P.,Hothorn, T., Boosting algorithms: Regularization, prediction and model fitting (with Discussion). Statistical Science,22 (2007) 477- 522.
[27] Khoshgftaar, T. M., Hulse, J. V., Napolitano, A., Comparing Boosting and Bagging Techniques with Noisy and Imbalanced Data. IEEE Transactions on Systems Man and Cybernetics, 41 (3) (2011) 552-568.
[28] Chen, Z., Lin, T., Chen, R., Xie Y., Xu, H., Creating diversity in ensembles using synthetic neighborhoods of training samples. Journal Apllied Intelligence, 47 (2) (2017) 570-583.
[29] Kotsiantis, S. B., Bagging and Boosting variants for handling classification problems: a survey. Cambridge University Press. 29 (1) (2014) 78- 100.
[30] Işıkhan, S., Mikrodizilim Gen İfade Çalışmalarında Genelleştirme Yöntemlerinin Regresyon Modelleri Üzerine Etkisi , PhD Thesis,. Hacettepe Üniversity, Ankara (2014)
[31] Dietterich, T., An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization. Machine Learning, 40(2) (2000) 139–157.
[32] Davidson, I., Fan, W., When Efficient Model Averaging Out- Performs Boosting and Bagging. 10th European Conference on Principles and Practice of Knowledge Discovery in Databases.Berlin, Germany, (2006) 477-486.
[33] Arsov, N.,Pavlovski, M., Basnarkov, L., Kocarev, L., 2017. Generating highly accurate prediction hypotheses through collaboratative ensemble learning. Scientific Reports, 7(44649) (2017) 1-34.

APA	TUĞ KAROĞLU T, Okut H (2020). Classification of the placement success in the undergraduate placement examination according to decision trees with bagging and boosting methods. , 93 - 105. 10.17776/csj.544639
Chicago	TUĞ KAROĞLU TUĞBA,Okut Hayrettin Classification of the placement success in the undergraduate placement examination according to decision trees with bagging and boosting methods. (2020): 93 - 105. 10.17776/csj.544639
MLA	TUĞ KAROĞLU TUĞBA,Okut Hayrettin Classification of the placement success in the undergraduate placement examination according to decision trees with bagging and boosting methods. , 2020, ss.93 - 105. 10.17776/csj.544639
AMA	TUĞ KAROĞLU T,Okut H Classification of the placement success in the undergraduate placement examination according to decision trees with bagging and boosting methods. . 2020; 93 - 105. 10.17776/csj.544639
Vancouver	TUĞ KAROĞLU T,Okut H Classification of the placement success in the undergraduate placement examination according to decision trees with bagging and boosting methods. . 2020; 93 - 105. 10.17776/csj.544639
IEEE	TUĞ KAROĞLU T,Okut H "Classification of the placement success in the undergraduate placement examination according to decision trees with bagging and boosting methods." , ss.93 - 105, 2020. 10.17776/csj.544639
ISNAD	TUĞ KAROĞLU, TUĞBA - Okut, Hayrettin. "Classification of the placement success in the undergraduate placement examination according to decision trees with bagging and boosting methods". (2020), 93-105. https://doi.org/10.17776/csj.544639

APA	TUĞ KAROĞLU T, Okut H (2020). Classification of the placement success in the undergraduate placement examination according to decision trees with bagging and boosting methods. Cumhuriyet Science Journal, 41(1), 93 - 105. 10.17776/csj.544639
Chicago	TUĞ KAROĞLU TUĞBA,Okut Hayrettin Classification of the placement success in the undergraduate placement examination according to decision trees with bagging and boosting methods. Cumhuriyet Science Journal 41, no.1 (2020): 93 - 105. 10.17776/csj.544639
MLA	TUĞ KAROĞLU TUĞBA,Okut Hayrettin Classification of the placement success in the undergraduate placement examination according to decision trees with bagging and boosting methods. Cumhuriyet Science Journal, vol.41, no.1, 2020, ss.93 - 105. 10.17776/csj.544639
AMA	TUĞ KAROĞLU T,Okut H Classification of the placement success in the undergraduate placement examination according to decision trees with bagging and boosting methods. Cumhuriyet Science Journal. 2020; 41(1): 93 - 105. 10.17776/csj.544639
Vancouver	TUĞ KAROĞLU T,Okut H Classification of the placement success in the undergraduate placement examination according to decision trees with bagging and boosting methods. Cumhuriyet Science Journal. 2020; 41(1): 93 - 105. 10.17776/csj.544639
IEEE	TUĞ KAROĞLU T,Okut H "Classification of the placement success in the undergraduate placement examination according to decision trees with bagging and boosting methods." Cumhuriyet Science Journal, 41, ss.93 - 105, 2020. 10.17776/csj.544639
ISNAD	TUĞ KAROĞLU, TUĞBA - Okut, Hayrettin. "Classification of the placement success in the undergraduate placement examination according to decision trees with bagging and boosting methods". Cumhuriyet Science Journal 41/1 (2020), 93-105. https://doi.org/10.17776/csj.544639