Classification Model Supervised Using the Heaviside Function

Authors

  • Andrés García Floriano Centro de Innovación y Desarrollo Tecnológico en Cómputo

DOI:

https://doi.org/10.13053/cys-23-4-3236

Keywords:

Pattern classification, supervised learning, heaviside function, non parametric statistical test

Abstract

In this paper, the theoretical foundations of a new classification model which is based on the associative approach of Pattern Recognition: Heaviside’s Classifier. As its name suggests its both phases, learning and classification, are based on the Heaviside’s function. The effectivity of the proposed model can be verified by the results of a comparative study where the classifier was tested against other seven pattern recognition models on 20 datasets. Experimental results indicate that the model is competitive in the state of the art. It is noteworthy that with one dataset, our classifier achieved the 100% of performance, validated with 10-fold cross-validation, while in its worst performance it achieved a little above of 50%. The obtained results were validated by the Wilcoxon non parametric test, which provides statistical certainty to the results of the performance comparison between models.

Author Biography

Andrés García Floriano, Centro de Innovación y Desarrollo Tecnológico en Cómputo

Unidad de Informática

Downloads

Published

2019-12-20

Issue

Vol. 23 No. 4 (2019): 23-4(2019)

Section

Report on PhD Thesis

License 

Hereby I transfer exclusively to the Journal "Computación y 
Sistemas", published by the Computing Research Center (CIC-IPN),
the Copyright of the aforementioned paper. I also accept that these 
rights will not be transferred to any other publication, in any other 
format, language or other existing means of developing.
I certify that the paper has not been previously disclosed or simultaneo
usly submitted to any other publication, and that it does not contain 
material whose publication would violate the Copyright or other 
proprietary rights of any person, company or institution. I certify that 
I have the permission from the institution or company where I work or 
study to publish this work.
The representative author accepts the responsibility for the publication
of this paper on behalf of each and every one of the authors. 
This transfer is subject to the following conditions:
  • The authors retain all ownership rights (such as patent rights) of this work, except for the publishing rights transferred to the CIC, through this document.
  • Authors retain the right to publish the work in whole or in part in any book they are the authors or publishers. They can also make use of this work in conferences, courses, personal web pages, and so on.
  • Authors may include working as part of his thesis, for non-profit distribution only.