Performance comparison of machine learning classification algorithms

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationAdvances in Computing and Data Science - Second International Conference, ICACDS 2018, Revised Selected Papers
EditorsTuncer Ören, P. K. Gupta, Jan Flusser, Vipin Tyagi, Mayank Singh
PublisherSpringer Verlag
Number of pages9
ISBN (Print)9789811318122
Publication statusPublished - 01-01-2018
Event2nd International Conference on Advances in Computing and Data Sciences, ICACDS 2018 - Dehradun, India
Duration: 20-04-201821-04-2018

Publication series

NameCommunications in Computer and Information Science
ISSN (Print)1865-0929


Conference2nd International Conference on Advances in Computing and Data Sciences, ICACDS 2018

All Science Journal Classification (ASJC) codes

  • General Computer Science
  • General Mathematics


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