Comparative analysis of Bayes net classifier, naive Bayes classifier and combination of both classifiers using WEKA

Автор: Abhilasha Nakra, Manoj Duhan

Журнал: International Journal of Information Technology and Computer Science @ijitcs

Статья в выпуске: 3 Vol. 11, 2019 года.

Бесплатный доступ

Authors here tried to use the WEKA tool to evaluate the performance of various classifiers on a dataset to come out with the optimum classifier, for a particular application. A Classifier is an important part of any machine learning application. It is required to classify various classes and get to know whether the predicted class lies in the true class. There are various performance analysis measures to judge the efficiency of a classifier and there are many tools which provide oodles of classifiers. In the present investigation, Bayes Net, Naive Bayes and their combination have been implemented using WEKA. It has been concluded that the combination of Bayes Net and Naive Bayes provides the maximum classification efficiency out of these three classifiers. Such a hybridization approach will always motivate for combining different classifiers to get the best results.

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Bayes Net, Naive Bayes, WEKA, Classifiers, Supervised, Unsupervised

Короткий адрес: https://sciup.org/15016343

IDR: 15016343   |   DOI: 10.5815/ijitcs.2019.03.04

Список литературы Comparative analysis of Bayes net classifier, naive Bayes classifier and combination of both classifiers using WEKA

  • M. Pérez-ortiz, S. Jiménez-fernández, and P. A. Gutiérrez, “A Review of Classification Problems and Algorithms,” https://doi.org/10.3390/en9080607, vol. 9 MDPI Ene, pp. 1–27, 2016.
  • R. Robu and C. Hora, “Medical Data Mining with extended WEKA,” in INES 2012 - IEEE 16th International Conference on Intelligent Engineering Systems, Proceedings, 2012, pp. 347–350, https://doi.org/10.1109/INES.2012.6249857
  • R. Duriqi, V. Raca, and B. Cico, “Comparative Analysis of Classification Algorithms on Three Different Datasets using WEKA,” 2016, http://dx.doi.org/10.1109/MECO.2016.7525775
  • A. K. Pandey and D. S. Rajpoot, “A Comparative Study of Classification Techniques by utilizing WEKA,” IEEE, pp. 219–224, 2016, https://doi.org/10.1109/ICSPCom.2016.7980579
  • M. C. Gunasekara, R.P.T.H Wijegunasekara and N. G. . Dias, “Comparison of Major Clustering Algorithms Using Weka Tool,” in International Conferences on Advances in ICT for Emerging Regions, 2014, p. 1, 10.1109/ICTER.2014.7083930
  • N. Kumar and S. Khatri, “Implementing WEKA for Medical Data Classification and Early Disease Prediction,” IEEE Int. Conf. "Computational Intell. Commun. Technol., vol. 3rd, pp. 1–6, 2017, https://doi.org/10.1109/CIACT.2017.7977277.
  • M. Ramzan, “Comparing and Evaluating the Performance of WEKA Classifiers on Critical Diseases,” IEEE, pp. 1–4, 2016, https://doi.org/10.1109/IICIP.2016.7975309
  • A. Jovic, K. Brkic, and N. Bogunovic, “An Overview of Free Software Tools for General Data Mining,” MIPRO, no. May, pp. 1112–1117, 2014, https://doi.org/10.1109/MIPRO.2014.6859735.
  • J. Mitrpanont, W. Sawangphol, T. Vithantirawat, and S. Paengkaew, “A Study on Using Python vs Weka on Dialysis Data Analysis,” Int. Conf. Inf. Technol., vol. 2nd, pp. 0–5, 2017, https://doi.org/10.1109/INCIT.2017.8257883.
  • P. Kaur, M. Singh, and G. Singh, “Classification and Prediction based Data Mining Algorithms to Predict Slow Learners in Education Sector,” Elsevier IRCTC, vol. 57, pp. 500–508, 2015.
  • P. Han, D. Wang, and Q. Zhao, “The Research on Chinese Document Clustering based on Weka,” Int. Conf. Mach. Learn. Cybern., pp. 10–13, 2011.
  • A. Sharma and B. Kaur, “A Research Review on Comparative Analysis of Data Mining Tools , Techniques and Parameters,” Int. J. Adv. Res. Comput. Sci., vol. 8, no. 7, pp. 523–529, 2017, http://dx.doi.org/10.26483/ijarcs.v8i7.4255.
  • G. K. M. Nookala, B. K. Pottumuthu, N. Orsu, and S. B. Mudunuri, “Performance Analysis and Evaluation of Different Data Mining Algorithms used for Cancer Classification,” Int. J. Adv. Res. Artif. Intell., vol. 2, no. 5, pp. 49–55, 2013, https://doi.org/10.14569/ijarai.2013.020508
  • M. Mayilvaganan and D.Kalpanadevi, “Comparison of Classification Techniques for Predicting the Performance of Students Academic Environment,” Int. Conf. Commun. Netw. Technol., pp. 113–118, 2014, http://dx.doi.org/10.1109/CNT.2014.7062736
  • V. Deepika. and N. Mishra, “Analysis and Prediction of Breast Cancer and Diabetes Disease Datasets using Data Mining Classification Techniques,” 2017 Int. Conf. Intell. Sustain. Syst., no. Iciss, pp. 533–538, 2017, https://doi.org/10.1109/iss1.2017.8389229
  • J. M. H. Priyadharshini, S. Kavitha, and B. Bharathi, “Classification and Analysis of Human Activities,” Int. Conf. Commun. Signal Process., pp. 1207–1211, 2017, https://doi.org/10.1109/iccsp.2017.8286571
  • R. R. Bouckaert et al., “WEKA Manual for Version 3-7-8,” Univ. WAIKATO, pp. 1–327, 2013, papers3://publication/uuid/24E005A2-AA1B-4614-BAF5-4D92C4F37413,
  • J. Brownlee, “How to Use Machine Learning Algorithms in Weka,” 2016. [Online]. Available: https://machinelearningmastery.com/use-machine-learning-algorithms-weka/,2:30pm,14th May 2018
  • “http://storm.cis.fordham.edu/~gweiss/data-mining/weka-data/diabetes.arff,” 1990. [Online]. Available: http://storm.cis.fordham.edu/~gweiss/data-mining/weka-data/diabetes.arff,2:30 pm 14thMay2018
  • K. Chai, H. T. Hn, and H. L. Cheiu, “Naive-Bayes Classification Algorithm,” Bayesian Online Classif. Text Classif. Filter., pp. 97–104, 2002.
  • R. R. Bouckaert et al., “WEKA - Experiences with a Java Open Source Project,” J. Mach. Learn. Res., vol. 11, pp. 2533–2541, 2010.
  • J. M. David and K. Balakrishnan, “Significance of Classification Techniques in Prediction of Learning Disabilities,” vol. 2576253, p. 10, 2010, https://doi.org/10.5121/ijaia.2010.1409
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