Metrics to guide a multi-objective evolutionary algorithm for ordinal classification

Research areas:

• Methodology - Machine learning

Year:

2014

Type of Publication:

Article

Keywords:

Mean absolute error,Multi-objective evolutionary algorithm,Ordinal measures,Ordinal classification,Ordinal regression,Proportional odds model

Authors:

• Cruz-Ramírez, Manuel
• Hervás-Martínez, César
• Sánchez-Monedero, Javier
• Gutiérrez, Pedro Antonio

Journal:

Neurocomputing

Volume:

135

Pages:

21-31

Month:

July

ISSN:

0925-2312

BibTex:

@article{CruzRamirez2014, author = "Manuel Cruz-Ram{\'i}rez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez and Javier S{\'a}nchez-Monedero and Pedro Antonio Guti{\'e}rrez", abstract = "Ordinal classification or ordinal regression are classification problems in which the labels have an ordered arrangement between them. Due to this order, alternative performance evaluation metrics are need to be used in order to consider the magnitude of errors. This paper presents an study of the use of a multi-objective optimization approach in the context of ordinal classification. We contribute a study of ordinal classification performance metrics, and propose a new performance metric, the Maximum Mean Absolute Error (MMAE). MMAE considers per-class distribution of patterns and the magnitude of the errors, both issues being crucial for ordinal regression problems. In addition we empirically show that some of the performance metrics are competitive objectives, which justifies the use of multi-objective optimization strategies. In our case, a multi-objective evolutionary algorithm optimizes a artificial neural network ordinal model with different pairs of metrics combinations, and we conclude that the pair of the Mean Absolute Error (MAE) and the proposed MMAE is the most favorable. A study of the relationship between the metrics of this proposal is performed, and the graphical representation in the 2 dimensional space where the search of the evolutionary algorithm takes place is analyzed. The results obtained show a good classification performance, opening new lines of research in the evaluation and model selection of ordinal classifiers.", awards = "JCR(2014): 2.083 Position: 36/123 (Q2) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", comments = "JCR(2014): 2.083 Position: 36/123 (Q2) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", doi = "10.1016/j.neucom.2013.05.058", issn = "0925-2312", journal = "Neurocomputing", keywords = "Mean absolute error,Multi-objective evolutionary algorithm,Ordinal measures,Ordinal classification,Ordinal regression,Proportional odds model", month = "July", note = "JCR(2014): 2.083 Position: 36/123 (Q2) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", pages = "21--31", title = "{M}etrics to guide a multi-objective evolutionary algorithm for ordinal classification", url = "http://dx.doi.org/10.1016/j.neucom.2013.05.058", volume = "135", year = "2014", }

Note:

JCR(2014): 2.083 Position: 36/123 (Q2) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Abstract:

Ordinal classification or ordinal regression are classification problems in which the labels have an ordered arrangement between them. Due to this order, alternative performance evaluation metrics are need to be used in order to consider the magnitude of errors. This paper presents an study of the use of a multi-objective optimization approach in the context of ordinal classification. We contribute a study of ordinal classification performance metrics, and propose a new performance metric, the Maximum Mean Absolute Error (MMAE). MMAE considers per-class distribution of patterns and the magnitude of the errors, both issues being crucial for ordinal regression problems. In addition we empirically show that some of the performance metrics are competitive objectives, which justifies the use of multi-objective optimization strategies. In our case, a multi-objective evolutionary algorithm optimizes a artificial neural network ordinal model with different pairs of metrics combinations, and we conclude that the pair of the Mean Absolute Error (MAE) and the proposed MMAE is the most favorable. A study of the relationship between the metrics of this proposal is performed, and the graphical representation in the 2 dimensional space where the search of the evolutionary algorithm takes place is analyzed. The results obtained show a good classification performance, opening new lines of research in the evaluation and model selection of ordinal classifiers.

Comments:

JCR(2014): 2.083 Position: 36/123 (Q2) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE