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2017 | OriginalPaper | Chapter

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

Authors : Dianne Arias, Yaima Filiberto, Rafael Bello, Ileana Cadena, Wilfredo Martinez

Published in: Applied Computer Sciences in Engineering

Publisher: Springer International Publishing

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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.

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previous chapter A Distance Measure Between Fuzzy Variables
next chapter Fuzzy Uncertainty in Random Variable Generation: A Cumulative Membership Function Approach
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Metadata
Title
Classification by Nearest Neighbor and Multilayer Perceptron a New Approach Based on Fuzzy Similarity Quality Measure: A Case Study
Authors
Dianne Arias
Yaima Filiberto
Rafael Bello
Ileana Cadena
Wilfredo Martinez
Copyright Year
2017
Book
Applied Computer Sciences in Engineering

Print ISBN: 978-3-319-66962-5

Electronic ISBN: 978-3-319-66963-2

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-66963-2_35

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