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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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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