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International Conference on Swarm Intelligence

ICSI 2016: Advances in Swarm Intelligence pp 79–88Cite as

An Empirical Evaluation of Machine Learning Algorithms for Image Classification

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9713))

Abstract

Image classification is an important aspect that needs techniques which can better predict or classify images as they become larger and complex to solve. Thus, the demand for research to find advanced algorithms and tools to solve problems experienced in classification, has shown great increase in interest over the years. The contribution of this paper is the evaluation of four machine learning techniques [multilayer perceptron (MLP), random forests (RF), k-Nearest Neighbor (k-NN), and the Naïve Bayes (NB)] in terms of classifying images. To this end, three industrial datasets are utilized against four performance measures (namely, precision, receiver operating characteristics, root mean squared error and mean absolute error). Experimental results show RF achieving higher accuracy while the NBC exhibits the worst performance.

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Acknowledgements

I would like to thank the University of Johannesburg for funding me and affording me the opportunity to utilize the resources to complete this work.

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering Science, University of Johannesburg, P.O. Box 524, Auckland Park, Johannesburg, 2006, South Africa

    Thembinkosi Nkonyana & Bhekisipho Twala

Authors
  1. Thembinkosi Nkonyana
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  2. Bhekisipho Twala
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Correspondence to Thembinkosi Nkonyana .

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Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Xi'an Jiaotong-Liverpool University, Suzhou, China

    Yuhui Shi

  3. Shenzen University, Shenzhen, China

    Li Li

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Nkonyana, T., Twala, B. (2016). An Empirical Evaluation of Machine Learning Algorithms for Image Classification. In: Tan, Y., Shi, Y., Li, L. (eds) Advances in Swarm Intelligence. ICSI 2016. Lecture Notes in Computer Science(), vol 9713. Springer, Cham. https://doi.org/10.1007/978-3-319-41009-8_8

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  • DOI: https://doi.org/10.1007/978-3-319-41009-8_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-41008-1

  • Online ISBN: 978-3-319-41009-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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