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Classification by Nearest Neighbor and Multilayer Perceptron a New Approach Based on Fuzzy Similarity Quality Measure: A Case Study

  • Dianne Arias
  • Yaima FilibertoEmail author
  • Rafael Bello
  • Ileana Cadena
  • Wilfredo Martinez
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 742)

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.

Keywords

Fuzzy similarity quality measure Similarity relation Classification Level of service of the road 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Dianne Arias
    • 1
  • Yaima Filiberto
    • 1
    Email author
  • Rafael Bello
    • 2
  • Ileana Cadena
    • 3
  • Wilfredo Martinez
    • 3
  1. 1.Department of Computer ScienceUniversidad de CamagüeyCamagüeyCuba
  2. 2.Department of Computer ScienceUniversidad Central de Las VillasSanta ClaraCuba
  3. 3.Department of Civil EngineeringUniversidad de CamagüeyCamagüeyCuba

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