Efficient Fog Prediction with Multi-objective Evolutionary Neural Networks

Áreas de investigación:

• Methodology - Machine learning

Año:

2018

Tipo de publicación:

Artículo

Autores:

• Durán-Rosal, Antonio Manuel
• Fernández, Juan Carlos
• Casanova-Mateo, Carlos
• Sanz-Justo, Julia
• Salcedo-Sanz, Sancho
• Hervás-Martínez, César

Journal:

Applied Soft Computing

Volumen:

70

Páginas:

347-358

Mes:

September

ISSN:

1568-4946

BibTex:

@article{Duran2018_fog, author = "Antonio Manuel Dur{\'a}n-Rosal and Juan Carlos Fern{\'a}ndez and Carlos Casanova-Mateo and Julia Sanz-Justo and Sancho Salcedo-Sanz and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "This paper proposes the application of novel artificial neural networks with evolutionary training and different basic functions, (sigmoidal, product and radial), for a real problem of fog events classification from meteorological input variables. Specifically, a Multiobjective Evolutionary Algorithm is considered as artificial neural network training mechanism in order to obtain a binary classification model for the detection of fog events at Valladolid airport (Spain). The evolutionary neural models developed are based on two-dimensional performance measures: traditional accuracy and the minimum sensitivity, as the lowest percentage of examples correctly predicted as belonging to each class with respect to the total number of examples in the corresponding class. These performance measures are directly related to features associated with any classifier: its global performance and the rate of the worst classified class. These two objectives are usually in conflict when the optimization process tries to construct models with a high classification rate level in the generalization dataset, and also with a good classification level for each class or minimum sensitivity. A sensitivity analysis of the proposed models is carried out, and thus the subjacent relations between the input variables and the output classification target can be better understood.", awards = "JCR(2018): 4.873 Position: 20/134 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", comments = "JCR(2018): 4.873 Position: 20/134 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", doi = "10.1016/j.asoc.2018.05.035", issn = "1568-4946", journal = "Applied Soft Computing", month = "September", note = "JCR(2018): 4.873 Position: 20/134 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", pages = "347-358", title = "{E}fficient {F}og {P}rediction with {M}ulti-objective {E}volutionary {N}eural {N}etworks", url = "doi.org/10.1016/j.asoc.2018.05.035", volume = "70", year = "2018", }

Nota:

JCR(2018): 4.873 Position: 20/134 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Abstract:

This paper proposes the application of novel artificial neural networks with evolutionary training and different basic functions, (sigmoidal, product and radial), for a real problem of fog events classification from meteorological input variables. Specifically, a Multiobjective Evolutionary Algorithm is considered as artificial neural network training mechanism in order to obtain a binary classification model for the detection of fog events at Valladolid airport (Spain). The evolutionary neural models developed are based on two-dimensional performance measures: traditional accuracy and the minimum sensitivity, as the lowest percentage of examples correctly predicted as belonging to each class with respect to the total number of examples in the corresponding class. These performance measures are directly related to features associated with any classifier: its global performance and the rate of the worst classified class. These two objectives are usually in conflict when the optimization process tries to construct models with a high classification rate level in the generalization dataset, and also with a good classification level for each class or minimum sensitivity. A sensitivity analysis of the proposed models is carried out, and thus the subjacent relations between the input variables and the output classification target can be better understood.

Comentarios:

JCR(2018): 4.873 Position: 20/134 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE