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Fuzzy Transform for Data Classification

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Book cover Fuzzy Transforms for Image Processing and Data Analysis

Abstract

Data classification is a data mining supervised learning process aimed to classify a set of data points or patterns.

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References

  1. Harrel, F. (2001). Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis (522 pp). New York: Springer. ISBN: 978-1-4419-2918-1.

    Google Scholar 

  2. Panda, M., & Matra, M. (2007). Network intrusion detection using Naive Bayes. International Journal of Computer Science and Network Security, 12, 258–263.

    Google Scholar 

  3. Dimitoglou, G., Adams, J.A., & Jim, C.M. (2013). Comparison of the C4.5 and a Naïve Bayes classifier for the prediction of lung cancer survivability. International Journal of Computer Science and Applications, 6(2), 256–261.

    Google Scholar 

  4. Aha, D.W. (Ed.) (1997). Lazy Learning (432 pp). Norwell (MA): Kluwer Academic Publishers. ISBN: 0792345843.

    Google Scholar 

  5. Jiang, L., Cai, Z., Wang, D., & Jiang, S. (2007). Survey of improving K-nearest-neighbor for Classification. In Fourth International Conference on Fuzzy Systems and Knowledge Discovery (5 pp). China: IEEE Computer Society Press. https://doi.org/10.1109/fskd.2007.552.

  6. Shalev-Shwartz, S., & Ben-David, S. (2014). Understanding Machine Learning (449 pp). Cambridge University Press. ISBN: 978-1-107-05713-5.

    Google Scholar 

  7. Ho, T.K. (1995). Random decision forests. In Proceedings of the 3rd International Conference on Document Analysis and Recognition (pp 278–282). Montreal, QC, 14–16 August 1995.

    Google Scholar 

  8. Ho, T. K. (1998). The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(8), 832–844. https://doi.org/10.1109/34.709601.

    Article  Google Scholar 

  9. Ho, T. K. (2002). A data complexity analysis of comparative advantages of decision forest constructors. Pattern Analysis and Applications, 5(2), 102–112.

    Article  MathSciNet  Google Scholar 

  10. Duda, R.O., Hart, P.E., Stork, D.G. (2001). Pattern Classification (2 ed., 617 pp). Wiley. ISBN 978-0471056690.

    Google Scholar 

  11. Pal, S.K., Mitra, S. (1992). Multilayer perceptron, fuzzy sets, and classification. IEEE Transactions on Neural Networks, 3(5), 683–697.

    Google Scholar 

  12. Chaudhuri, C. B., & Bhattacharya, U. (2007). Efficient training and improved performance of multilayer perceptron in pattern classification. Neurocomputing, 34, 11–27. https://doi.org/10.1016/S09252312(00)003052.

    Article  MATH  Google Scholar 

  13. Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. In Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence (Vol. 2, No. 12, pp. 1137–1143). San Mateo, CA: Morgan Kaufmann.

    Google Scholar 

  14. Tukey, J. (1958). Bias and confidence in not quite large samples. Annals of Mathematical Science, 29, 614.

    Google Scholar 

  15. Wong, T. T. (2015). Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation. Pattern Recognition, 48(9), 2839–2846.

    Article  Google Scholar 

  16. Wong, T. T. (2017). Parametric methods for comparing the performance of two classification algorithms evaluated by K-fold cross validation on multiple data sets. Pattern Recognition, 65, 97–107.

    Article  Google Scholar 

  17. Di Martino, F., Sessa, S. (2020). A classification algorithm based on multi-dimensional fuzzy transforms. Journal of Ambient Intelligence and Humanized Computing (in press).

    Google Scholar 

  18. Di Martino, F., Loia, V., & Sessa, S. (2010). Fuzzy transforms method and attribute dependency in data analysis. Information Sciences, 180(4), 493–505.

    Article  MathSciNet  Google Scholar 

  19. Mitchell, T.M. (1997). Machine Learning (414 pp). Mc Graw-Hill, New York. ISBN: 9780070428072.

    Google Scholar 

  20. Bhargawa, N., Sharma, G., Bhargava, R., & Mathuria, M. (2013). Decision tree analysis on j48 algorithm for data mining. International Journal of Advanced Research in Computer Science and Software Engineering, 3(6), 1114–1119.

    Google Scholar 

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Authors and Affiliations

  1. Department of Architecture, University of Naples Federico II, Naples, Italy

    Ferdinando Di Martino

  2. Department of Architecture, University of Naples Federico II, Naples, Italy

    Salvatore Sessa

Authors
  1. Ferdinando Di Martino
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  2. Salvatore Sessa
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Correspondence to Ferdinando Di Martino .

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Di Martino, F., Sessa, S. (2020). Fuzzy Transform for Data Classification. In: Fuzzy Transforms for Image Processing and Data Analysis. Springer, Cham. https://doi.org/10.1007/978-3-030-44613-0_11

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  • DOI: https://doi.org/10.1007/978-3-030-44613-0_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-44612-3

  • Online ISBN: 978-3-030-44613-0

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