Veena, K M and Shenoy, Manjula K and Shenoy, Ajitha K B (2018) Performance Comparison of Machine Learning Classification Algorithms. In: ICACDS 2018: Advances in Computing and Data Sciences. Springer, pp. 489-497.

PDF 129.pdf - Published Version Restricted to Registered users only Download (308kB) | Request a copy

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.

Actions (login required)

View Item