Towards the use of vector based GP to predict physiological time series

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@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