Abstract
In this paper the performance of k Nearest Neighbors and Multilayer Perceptron algorithm the is used in a classical task in the branch of the Civil Engineering: predict the level of service in the road. The use of fuzzy similarity quality measure method for calculating the weights of the features allows to performance of KNN and MLP in the case of mixed data (features with discrete or real domains). Experimental results show that this approach is better than other methods used to calculate the weight of the features. The results of the predictions of the level of service show the effectiveness of the method in the solution of problems of traffic engineering.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Filiberto, Y., Bello, R., Caballero, Y., Larrua, R.: A method to build similarity relations into extended rough set theory. In: 10th International Conference on Intelligent Systems Design and Applications (2010)
Fu, X., Zhang, S., Pang, Z.: A resource limited immune approach for evolving architecture and weights of multilayer neural network. In: Tan, Y., Shi, Y., Tan, K.C. (eds.) ICSI 2010, Part I. LNCS, vol. 6145, pp. 328–337. Springer, Heidelberg (2010)
Adam, S., Alexios, D., Vrahatis, M.: Revisiting the problem of weight initialization for multi-layer perceptrons trained with back propagation. In: Köppen, M., et al. (eds.) ICONIP 2008, Part II. LNCS, vol. 5507, pp. 308–315. Springer, Heidelberg (2009)
Coello, L., Fernandez, Y., Filiberto, Y., Bello, R.: Improving the MLP learning by using a method to calculate the initial weights with the quality of similarity measure based on fuzzy sets and particle swarms. J. CyS. 19, 309–320 (2015)
Filiberto, Y., Bello, R., Caballero, Y., Larrua, R.: Using PSO and RST to predict the resistant capacity of connections in composite structures. In: González, J.R., Pelta, D.A., Cruz, C., Terrazas, G., Krasnogor, N. (eds.) NICSO 2010. SCI, vol. 284, pp. 359–370. Springer, Heidelberg (2010)
Mitchell, T.: Machine learning. In: Science/Engineering/Math. McGraw Hill, Portland (1997)
Fernandez, Y., Coello, L., Filiberto, Y., Bello, R., Falco, R.: Learning similarity measures from data with fuzzy sets and particle swarms. In: 11th International Conference on Electrical Engineering, Computing Science and Automatic Control, pp. 1–6. IEEE Press, Mexico City (2014)
Duch, W., Grudzinski, K.: Weighting and selection features. In: Intelligent Information Systems, pp. 32–36 (1999)
Filiberto, Y., Bello, R., Caballero, Y., Frias, M.: An analysis about the measure quality of similarity and its applications in machine learning. In: 4th International Workshop on Knowledge Discovery, Knowledge Management and Decision Support, Mexico, pp. 130–139 (2013)
Zadeh, L.A.: Similarity relations and fuzzy orderings. Inf. Sci. 3, 177–200 (1971)
Wang, W.: New similarity measures on fuzzy sets and on elements. Fuzzy Sets Syst. 85, 305–309 (1997)
Iman, R., Davenport, J.: Approximations of the critical region of the friedman statistic. Commun. Stat. Part A Theor. Meth. 9, 571–595 (1980)
Holm, S.: A simple sequentially rejective multiple test procedure. J. Stat. 6, 65–70 (1979)
Cal, R., Reyes, M., Cardenas, J.: Ingenieria deTransito. Fundamentos y Aplicaciones. Felix Varela, La Habana (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Arias, D., Filiberto, Y., Bello, R., Cadena, I., Martinez, W. (2017). Classification by Nearest Neighbor and Multilayer Perceptron a New Approach Based on Fuzzy Similarity Quality Measure: A Case Study. In: Figueroa-García, J., López-Santana, E., Villa-Ramírez, J., Ferro-Escobar, R. (eds) Applied Computer Sciences in Engineering. WEA 2017. Communications in Computer and Information Science, vol 742. Springer, Cham. https://doi.org/10.1007/978-3-319-66963-2_35
Download citation
DOI: https://doi.org/10.1007/978-3-319-66963-2_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-66962-5
Online ISBN: 978-3-319-66963-2
eBook Packages: Computer ScienceComputer Science (R0)