An Experimental Study of Evolutionary Product-Unit Neural Network Algorithm

Authors

  • Alain Guerrero-Enamorado Universidad de las Ciencias Informaticas (UCI)
  • Daimerys Ceballos-Gastell Universidad de las Ciencias Informaticas (UCI)

DOI:

https://doi.org/10.13053/cys-20-2-2218

Keywords:

Evolutionary Product-Unit Neural Network (EPUNN), missing values, imbalanced data, noisy data.

Abstract

This paper aims to obtain empirical information about the behavior of an Evolutionary Product-Unit Neural Network (EPUNN) in different scenarios. To achieve this, an extensive evaluation was conducted on 21 data sets for the classification task. Then, we evaluated EPUNN on eleven noisy data sets, on sixteen imbalanced data sets, and on ten missing values data sets. As a result of this evaluation process, we conclude that there does not exist a significant difference between EPUNN and the four algorithms assessed; the accuracy of EPUNN rapidly worsen in the presence of noise, so we do not recommend its utilization in noisy environments; we found a tendency to robustness in EPUNN while the imbalance ratio grows; finally, we can state that it is able to handle missing data, but in this kind of data, a significant performance deterioration was manifested. For future work, we recommend to assess the impact of irrelevant attributes on EPUNN performance. In addition, an extension of noisy data set evaluation would be opportune.

Author Biographies

Alain Guerrero-Enamorado, Universidad de las Ciencias Informaticas (UCI)

Alain Guerrero-Enamorado received his B.Sc. degree in Automatic Control from the Universidad de Oriente in 2003. He got a Master degree in Applied Informatics from the Universidad de las Ciencias Informáticas in 2009. He is currently an assistant professor at the Department of Programming Techniques of Faculty 1 of Universidad de las Ciencias Informáticas. Guerrero-Enamorado is a doctoral student in the Knowledge Discovery and Intelligent Systems Group, his main research  interests are in the fields of machine learning, data mining, and their applications.

Daimerys Ceballos-Gastell, Universidad de las Ciencias Informaticas (UCI)

Daimerys Ceballos-Gastell received her B.Sc. degree in Computer Engineering from the Universidad de las Ciencias Informáticas in 2008. She is currently an assistant professor at the Department of Programming Techniques in Faculty 1 of the Universidad de las Ciencias Informáticas. Ceballos-Gastell is a master student in Applied Informatics, her main research interests are in the fields of artificial neural networks, machine learning, data mining, and their applications.

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Published

2016-06-25

Issue

Vol. 20 No. 2 (2016): 20-2 (2016)

Section

Articles

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