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A Comparative Performance of Classification Algorithms in Predicting Alcohol Consumption Among Secondary School Students

  • Dilip Singh Sisodia
  • Reenu Agrawal
  • Deepti Sisodia
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 748)

Abstract

The increased consumption of alcohol among secondary school students has been a matter of concern these days. Alcoholism not only affects individual’s decision-making ability but also have a negative effect on academic performance. The early prediction of a student consuming alcohol can be helpful in preventing them from such risks and failures. This paper evaluates classification algorithms for prediction of certain risks of secondary school student due to alcohol consumption. The classification algorithms considered here are three individual classifiers including Naïve Bayes Classifier, Random Tree, Simple Logistic and three ensemble classifiers: Random Forest, Bagging, and Adaboost. The dataset is taken from the UCI repository. The performance of these algorithms is evaluated using standard evaluation metrics such as Accuracy, Precision, Recall and F-Measure. The results suggested that Simple Logistic and Random Forest performed better than the other classifiers.

Keywords

Alcohol consumption Classifiers Performance measures Prediction Ensemble learners 

References

  1. 1.
    Bateman, M.: Does alcohol cause breast cancer. http://www.drinkaware.co.uk/alcohol-and-you/health/does-alcohol-cause-breast-cancer (2011). Accessed 11, 2011
  2. 2.
    Spear, L.P.: Alcohol’s effects on adolescents. Alcohol Res. Health 26, 287–291 (2002)Google Scholar
  3. 3.
    Pagnotta, F., Amran, H.M.: Using data mining to predict secondary school student alcohol consumption. Department of Computer Science, University of Camerino (2016)Google Scholar
  4. 4.
    Cortez, P., Silva, A.M.G.: Using data mining to predict secondary school student performance (2008)Google Scholar
  5. 5.
    Bi, J., Sun, J., Wu, Y., Tennen, H., Armeli, S.: A machine learning approach to college drinking prediction and risk factor identification. ACM Trans. Intell. Syst. Technol. (TIST) 4, 72 (2013)Google Scholar
  6. 6.
    Sharma, M., Deb, D., Acharya, U.R.: A novel three-band orthogonal wavelet filter bank method for an automated identification of alcoholic EEG signals. Appl. Intell. 1–11 (2017)Google Scholar
  7. 7.
    Sharma, M., Pachori, R.: A novel approach to detect epileptic seizures using a combination of tunable-Q wavelet transform and fractal dimension. J. Mech. Med. Biol. 1740003 (2017)Google Scholar
  8. 8.
    AL-Nabi, D.L.A., Ahmed, S.S.: Survey on classification algorithms for data mining: comparison and evaluation. Int. J. Comput. Eng. Intell. Syst. 4, 18–27 (2013)Google Scholar
  9. 9.
    Murphy, K.P.: Naive bayes classifiers. University of British Columbia (2006)Google Scholar
  10. 10.
    Jagannathan, G., Pillaipakkamnatt, K., Wright, R.N.: A practical differentially private random decision tree classifier. In: IEEE International Conference on Data Mining Workshops, ICDMW’09, pp. 114–121 (2009)Google Scholar
  11. 11.
    Feng, J., Xu, H., Mannor, S., Yan, S.: Robust logistic regression and classification. In: Proceedings of Advances in Neural Information Processing Systems, pp. 253–261 (2014)Google Scholar
  12. 12.
    Gãš, B.: Analysis of a random forests model. J. Mach. Learn. Res. 13, 1063–1095 (2012)Google Scholar
  13. 13.
    Liaw, A., Wiener, M., et al.: Classification and regression by randomForest. R News 2, 18–22 (2002)Google Scholar
  14. 14.
    Loh, W.-Y.: Classification and regression trees. Wiley Interdiscip. Rev. Data Mining Knowl. Discov. 1, 14–23 (2011)CrossRefGoogle Scholar
  15. 15.
    Breiman, L.: Bagging predictors. Mach. Learn. 24, 123–140 (1996)zbMATHGoogle Scholar
  16. 16.
    Breiman, L.: Out-of-bag estimation (1996)Google Scholar
  17. 17.
    Falaki, H.: AdaBoost algorithm. Startrinity, 202 (2009). http://startrinity.com/VideoRecognition/Resources/Adaboost/boosting%20algorithm
  18. 18.
    Freund, Y., Schapire, R.E., et al.: Experiments with a new boosting algorithm. In: Proceedings of ICML, pp. 148–156 (1996)Google Scholar
  19. 19.
    Eberhardinger, B., Anders, G., Seebach, H., Siefert, F., Reif, W.: A research overview and evaluation of performance metrics for self-organization algorithms. In: 2015 IEEE International Conference on Self-Adaptive and Self-Organizing Systems Workshops (SASOW), pp. 122–127 (2015)Google Scholar
  20. 20.
    Tiwari, M., Jha, M.B., Yadav, O.: Performance analysis of data mining algorithms in Weka. IOSR J. Comput. Eng. 6, 32–41 (2012)CrossRefGoogle Scholar
  21. 21.
    Witten, I.H., Frank, E., Hall, M.A., Pal, C.J.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann (2016)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Dilip Singh Sisodia
    • 1
  • Reenu Agrawal
    • 1
  • Deepti Sisodia
    • 1
  1. 1.National Institute of Technology RaipurRaipurIndia

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