Abstract
Big data analytics is considered to be the future of information technology in today's world, which incorporates data mining to be one of its most promising tool. The present work illustrates a comparative study to find out which kind of classifiers work better with which kind of datasets. It illustrates comparisons of the efficiency of the different classifiers focusing on numeric and text data. Datasets from IMDb and 20newsgroups have been used for the purpose. Current work mainly focuses on comparing different algorithms such as Decision Stump, Decision Table, K-Star, REPTree and ZeroR in the area of numeric classification, and evaluation of the efficiency of Naive Bayes classifier for text classification. The result in this paper suggests the best and worst of the test parameters, as it widens the scope of their usage on the basis of types and the size of datasets.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bin Othman, M.F., Yau, T.M.S.: Comparison of different classification techniques using WEKA for breast cancer. In: 3rd Kuala Lumpur International Conference on Biomedical Engineering 2006, pp. 520–523. Springer, Berlin, Heidelberg (2007)
Deepajothi, S., Selvarajan, S.: A comparative study of classification techniques on adult data set. Int. J. Eng. Res. Technol. (IJERT) 1 (2012)
Vaithiyanathan, V., Rajeswari, K., Tajane, K., Pitale, R.: Comparison of different classification techniques using different datasets. Int. J. Adv. Eng. Technol. 6(2), 764 (2013)
Bouali, H., Akaichi, J.: Comparative study of different classification techniques: heart disease use case. In: 2014 13th International Conference on Machine Learning and Applications (ICMLA), pp. 482–486. IEEE (2014)
Shinge, S., Zarekar, R., Vaithiyanathan, V., Rajeswari, K.: Comparative analysis of heart risk dataset using different classification techniques. Int. J. Res. Inf. Technol. 489–494 (2015)
Raheja, S., Munjal, G.: Analysis of Linux Kernel vulnerabilities. Ind. J. Sci. Technol. 9(48) (2016)
Arundthathi, A., Vijayaselvi, G., Savithri, V.: Assessment of decision tree algorithms on student’s recital (2017)
Nadu, T.: Cad Diagnosis Using PSO. BAT, MLR and SVM (2017)
Ikonomakis, M., Kotsiantis, S., Tampakas, V.: Text classification using machine learning techniques. WSEAS Trans. Comput. 4(8), 966–974 (2005)
Kotsiantis, S.B., Zaharakis, I., Pintelas, P.: Supervised machine learning: a review of classification techniques. Emer. Artif. Intell. Appl. Comput. Eng. 160, 3–24 (2007)
Ting, S.L., Ip, W.H., Tsang, A.H.: Is Naive Bayes a good classifier for document classification. Int. J. Softw. Eng. Appl. 5(3), 37–46 (2011)
Wahbeh, A.H., Al-Kabi, M.: Comparative assessment of the performance of three WEKA text classifiers applied to arabic text. Abhath Al-Yarmouk: Basic Sci. Eng. 21(1), 15–28 (2012)
Purohit, A., Atre, D., Jaswani, P., Asawara, P.: Text classification in data mining. Int. J. Sci. Res. Publ. 5(6), 1–7 (2015)
Rajeswari, R.P., Juliet, K., Aradhana: Text classification for student data set using Naive Bayes classifier and KNN classifier. Int. J. Comput. Trends Technol. (IJCTT) 43(1) (2017)
Tilve, A.K.S., Jain, S.N.: A survey on machine learning techniques for text classification. Int. J. Eng. Sci. Res. Technol. (2017)
Mandal, L., Das, R., Bhattacharya, S., Basu, P.N.: Intellimote: a hybrid classifier for classifying learners’ emotion in a distributed e-learning environment. Turkish J. Electr. Eng. Comput. Sci. 25(3), 2084–2095 (2017)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Kumar, N., Mitra, S., Bhattacharjee, M., Mandal, L. (2019). Comparison of Different Classification Techniques Using Different Datasets. In: Chakraborty, M., Chakrabarti, S., Balas, V., Mandal, J. (eds) Proceedings of International Ethical Hacking Conference 2018. Advances in Intelligent Systems and Computing, vol 811. Springer, Singapore. https://doi.org/10.1007/978-981-13-1544-2_22
Download citation
DOI: https://doi.org/10.1007/978-981-13-1544-2_22
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1543-5
Online ISBN: 978-981-13-1544-2
eBook Packages: EngineeringEngineering (R0)