Multiobjective Pareto Ordinal Classification for Predictive Microbiology

Áreas de investigación:

• Methodology - Machine learning

Año:

2012

Tipo de publicación:

Artículo en conferencia

Autores:

• Cruz-Ramírez, Manuel
• Fernández, Juan Carlos
• Valero, Antonio
• Gutiérrez, Pedro Antonio
• Hervás-Martínez, César

Volumen:

188

Título del libro:

7th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO'12)

Serie:

Advances in Intelligent Systems and Computing

Páginas:

153-162

Organización:

Ostrava, Czech Republic

Mes:

5-7 September

BibTex:

@conference{MOEAbiolCruz2012, author = "Manuel Cruz-Ram{\'i}rez and Juan Carlos Fern{\'a}ndez and Antonio Valero and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "This paper proposes the use of a Memetic Multiobjective Evolutionary Algorithm (MOEA) based on Pareto dominance to solve two ordinal classification problems in predictive microbiology. Ordinal classification problems are those ones where there is order between the classes because of the nature of the problem. Ordinal classification algorithms may take advantage of this situation to improve its classification. To guide the MOEA, two non-cooperative metrics have been used for ordinal classification: the Average of the Mean Absolute Error, and the Maximum Mean Absolute Error of all the classes. The MOEA uses an ordinal regression model with Artificial Neural Networks to classify the growth classes of microorganisms such as Listeria monocytogenes and Staphylococcus aureus. ", booktitle = "7th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO{\&}#039;12)", month = "5-7 September", organization = "Ostrava, Czech Republic", pages = "153-162", series = "Advances in Intelligent Systems and Computing", title = "{M}ultiobjective {P}areto {O}rdinal {C}lassification for {P}redictive {M}icrobiology", volume = "188", year = "2012", }

Abstract:

This paper proposes the use of a Memetic Multiobjective Evolutionary Algorithm (MOEA) based on Pareto dominance to solve two ordinal classification problems in predictive microbiology. Ordinal classification problems are those ones where there is order between the classes because of the nature of the problem. Ordinal classification algorithms may take advantage of this situation to improve its classification. To guide the MOEA, two non-cooperative metrics have been used for ordinal classification: the Average of the Mean Absolute Error, and the Maximum Mean Absolute Error of all the classes. The MOEA uses an ordinal regression model with Artificial Neural Networks to classify the growth classes of microorganisms such as Listeria monocytogenes and Staphylococcus aureus.