Published June 13, 2016
| Version v1
Publication
Improving the Accuracy of a Two-Stage Algorithm in Evolutionary Product Unit Neural Networks for Classification by Means of Feature Selection
Description
This paper introduces a methodology that improves the accuracy
of a two-stage algorithm in evolutionary product unit neural networks
for classification tasks by means of feature selection. A couple
of filters have been taken into consideration to try out the proposal.
The experimentation has been carried out on seven data sets from the
UCI repository that report test mean accuracy error rates about twenty
percent or above with reference classifiers such as C4.5 or 1-NN. The
study includes an overall empirical comparison between the models obtained
with and without feature selection. Also several classifiers have
been tested in order to illustrate the performance of the different filters
considered. The results have been contrasted with nonparametric statistical
tests and show that our proposal significantly improves the test
accuracy of the previous models for the considered data sets. Moreover,
the current proposal is much more efficient than a previous methodology
developed by us; lastly, the reduction percentage in the number of inputs
is above a fifty five, on average.
Abstract
MICYT TIN2007-68084-C02-02Abstract
MICYT TIN2008-06681-C06-03Abstract
Junta de Andalucía P08-TIC-3745Additional details
Identifiers
- URL
- https://idus.us.es/handle/11441/42174
- URN
- urn:oai:idus.us.es:11441/42174