Abstract
Machine learning (ML) is to make logical patterns out of various types of input data including images, texts, numbers and any other types of data. Data derived from research will be processes through machine learning algorithms and leads to a prediction that is mainly considered as the output of the machine learning algorithm. In this paper, the most popular learning algorithms have been reviewed and their specific features are discussed to help select the most appropriate algorithm through comparison in different research projects. However, there is not just one efficient method to apply to all data sets, and the appropriate algorithm may differ based on factors in a study.
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
Similar content being viewed by others
References
S. Amornsamankul, B. Pimpunchat, W. Triampo et al., A comparison of machine learning algorithms and their applications. Int. J. Simul. Syst. Sci. Technol. 8, 1–17 (2019)
K. Das, R.N. Behera, A survey on machine learning: concept, algorithms and applications. Int. J. Innov. Res. Comput. Commun. Eng. 5(2), 1301–1309 (2017)
V. Sze, Y. Chen, T. Yang et al., Efficient processing of deep neural networks: a tutorial and survey. Proceed. IEEE 105(12), 2295–2329 (2017)
M. Sokolova, G. Lapalme, Performance measures in classification of human communications. Advances in Artificial Intelligence (Springer, Berlin, 2007)
S. Kotsiantis, Supervised machine learning: a review of classification techniques. Informatica (Slovenia) 31, 249–268 (2007)
X. Li, C.W. Chan, H.H. Nguyen, Application of the neural decision tree approach for prediction of petroleum production. J. Petrol. Sci. Eng. 104, 11–16 (2013)
F. Butaru, Q. Chen, B. Clark et al., Risk and risk management in the credit card industry. J. Bank. Finance 72, 218–239 (2016)
Z. Omary, F. Mtenzi, Machine learning approach to identifying the dataset threshold for the performance estimators in supervised learning. Int. J. Infonom. 3(3), 314–325 (2010)
S. Raschka, Naive Bayes and Text Classification I Introduction and Theory (2014). Available at: https://arxiv.org/pdf/1410.5329. Accessed Aug 2021
M.C. Belavagi, B. Muniyal, Performance evaluation of supervised machine learning algorithms for intrusion detection. Procedia Comput. Sci. 89, 117–123 (2016)
P. Reed, Y. Wu, Logistic regression for risk factor modelling in stuttering research. J. Fluency Disord. 38(2), 88–101 (2013)
L. Breiman, Random forests. Mach. Learn. 45(1), 5–32 (2001)
J.A. Hartigan, M.A. Wong, Algorithm AS 136: A K-means clustering algorithm. J. Royal Stat. Soc. Series C (Appl. Stat.). 28(1), 100–108 (1979)
S. Shalev-Shwartz, Y. Singer, N. Srebro et al., Pegasos: primal estimated sub-gradient solver for SVM. Math. Program. 127(1), 3–30 (2010)
K. Pearson, LIII. On lines and planes of closest fit to systems of points in space. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 2(11), 559–572 (1901)
X. Zhu, A.B. Goldberg, Introduction to Semi-Supervised Learning (Morgan & Claypool Publishers, 2009)
N. Kasabov, S. Pang, Transductive support vector machines and applications in bioinformatics for promoter recognition, in Proceedings of the 2003 International Conference on Neural Networks and Signal Processing, vol. 1 (2004), pp. 1–6
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Taherdoost, H. (2022). Classification of Machine Learning Algorithms. In: Verma, P., Charan, C., Fernando, X., Ganesan, S. (eds) Advances in Data Computing, Communication and Security. Lecture Notes on Data Engineering and Communications Technologies, vol 106. Springer, Singapore. https://doi.org/10.1007/978-981-16-8403-6_38
Download citation
DOI: https://doi.org/10.1007/978-981-16-8403-6_38
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-8402-9
Online ISBN: 978-981-16-8403-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)