Towards the use of vector based GP to predict physiological time series
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @Article{AZZALI:2020:ASC,
-
author = "Irene Azzali and Leonardo Vanneschi and
Illya Bakurov and Sara Silva and Marco Ivaldi and Mario Giacobini",
-
title = "Towards the use of vector based {GP} to predict
physiological time series",
-
journal = "Applied Soft Computing",
-
volume = "89",
-
pages = "106097",
-
year = "2020",
-
ISSN = "1568-4946",
-
DOI = "doi:10.1016/j.asoc.2020.106097",
-
URL = "http://www.sciencedirect.com/science/article/pii/S1568494620300375",
-
keywords = "genetic algorithms, genetic programming, Ventilation,
Physiological data, Machine learning, Time series",
-
abstract = "Prediction of physiological time series is frequently
approached by means of machine learning (ML)
algorithms. However, most ML techniques are not able to
directly manage time series, thus they do not exploit
all the useful information such as patterns, peaks and
regularities provided by the time dimension. Besides
advanced ML methods such as recurrent neural network
that preserve the ordered nature of time series, a
recently developed approach of genetic programming,
VE-GP, looks promising on the problem in analysis.
VE-GP allows time series as terminals in the form of a
vector, including new strategies to exploit this
representation. In this paper we compare different ML
techniques on the real problem of predicting
ventilation flow from physiological variables with the
aim of highlighting the potential of VE-GP.
Experimental results show the advantage of applying
this technique in the problem and we ascribe the good
performances to the ability of properly catching
meaningful information from time series",
- }
Genetic Programming entries for
Irene Azzali
Leonardo Vanneschi
Illya Bakurov
Sara Silva
Marco Ivaldi
Mario Giacobini
Citations