Abstract
Classification of binary and multi-class datasets to draw meaningful decisions is the key in today’s scientific world. Machine learning algorithms are known to effectively classify complex datasets. This paper attempts to study and compare the classification performance if four supervised machine learning classification algorithms, viz., “Classification And Regression Trees, k-Nearest Neighbor, Support Vector Machines and Naive Bayes” to five different types of data sets, viz., mushrooms, page-block, satimage, thyroid and wine. The classification accuracy of each algorithm is evaluated using the 10-fold cross-validation technique. “The Classification And Regression Tree” algorithm is found to give the best classification accuracy.
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References
Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13(1), 21–27 (1967)
Mucherino, A., Papajorgji, P.J., Pardalos, P.M.: k-nearest neighbor classification. In: Mucherino, A., Papajorgji, P.J., Pardalos, P.M. (eds.) Data Mining in Agriculture, pp. 83–106. Springer, New York (2009). https://doi.org/10.1007/978-0-387-88615-2_4
Breiman, L., Friedman, J., Olshen, R., Stone, C.: Classification and Regression Trees. Wadsworth and Brooks, Monterey (1984)
Unda-Trillas, E., Rivera-Rovelo, J.: A Method to Build Classification and Regression Trees. In: Bayro-Corrochano, E., Hancock, E. (eds.) CIARP 2014. LNCS, vol. 8827, pp. 448–453. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-12568-8_55
Trendowicz, A., Jeffery, R.: Classification and Regression Trees, pp. 295–304. Springer, Cham (2014)
Berk, R.A.: Classification and regression trees (CART). In: Berk, R.A. (ed.) Statistical Learning from a Regression Perspective, 129–186. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-44048-4_3
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley, London (2001)
Webb, G.I.: Naïve Bayes. In: Sammut, C., Webb, G.I. (eds.) Encyclopedia of Machine Learning, pp. 713–714. Springer, Boston (2011). https://doi.org/10.1007/978-0-387-30164-8_576
Kotsiantis, S.B., Zaharakis, I., Pintelas, P.: Supervised machine learning: a review of classification techniques (2007)
Kirk, M.: Thoughtful Machine Learning: A Test-Driven Approach. O’Reilly Media, Inc., Newton (2014)
Ben-Hur, A., Weston, J.: A User’s Guide to Support Vector Machines, pp. 223–239. Humana Press, Totowa (2010)
Vapnik, V.N., Vapnik, V.: Statistical Learning Theory, vol. 1. Wiley, New York (1998)
Kreßel, U.H.-G.: Pairwise classification and support vector machines. In: Schölkopf, B., Burges, C.J.C., Smola, A.J. (eds.) Advances in Kernel Methods, pp. 255–268. MIT Press, Cambridge (1999)
Wang, Z., Xue, X.: Multi-class support vector machine. In: Ma, Y., Guo, G. (eds.) Support Vector Machines Applications, pp. 23–48. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-02300-7_2
KEEL dataset repository. http://sci2s.ugr.es/keel/category.php?cat=clas&order=clas#sub2. Accessed 25 Jan 2018
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Veena, K.M., Manjula Shenoy, K., Ajitha Shenoy, K.B. (2018). Performance Comparison of Machine Learning Classification Algorithms. In: Singh, M., Gupta, P., Tyagi, V., Flusser, J., Ören, T. (eds) Advances in Computing and Data Sciences. ICACDS 2018. Communications in Computer and Information Science, vol 906. Springer, Singapore. https://doi.org/10.1007/978-981-13-1813-9_49
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DOI: https://doi.org/10.1007/978-981-13-1813-9_49
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