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Memorias de investigación
Artículos en revistas:
Cost-sensitive selective naive Bayes classifiers for predicting the increase of the h-index for scientific journals
Año:2014
Áreas de investigación
  • Inteligencia artificial
Datos
Descripción
Machine learning community is not only interested in maximizing classification accuracy, but also in minimizing the distances between the actual and the predicted class. Some ideas, like the cost-sensitive learning approach, are proposed to face this problem. In this paper, we propose two greedy wrapper forward cost-sensitive selective naive Bayes approaches. Both approaches readjust the probability thresholds of each class to select the class with the minimum-expected cost. The first algorithm (CS-SNB-Accuracy) considers adding each variable to the model and measures the performance of the resulting model on the training data. The variable that most improves the accuracy, that is, the percentage of well classified instances between the readjusted class and actual class, is permanently added to the model. In contrast, the second algorithm (CS-SNB-Cost) considers adding variables that reduce the misclassification cost, that is, the distance between the readjusted class and actual class. We have tested our algorithms on the bibliometric indices prediction area. Considering the popularity of the well-known h-index, we have researched and built several prediction models to forecast the annual increase of the h-index for Neurosciences journals in a four-year time horizon. Results show that our approaches, particularly CS-SNB-Accuracy, achieved higher accuracy values than the analyzed cost-sensitive classifiers and Bayesian classifiers. Furthermore, we also noted that the CS-SNB-Cost always achieved a lower average cost than all analyzed cost-sensitive and cost-insensitive classifiers. These cost-sensitive selective naive Bayes approaches outperform the selective naive Bayes in terms of accuracy and average cost, so the cost-sensitive learning approach could be also applied in different probabilistic classification approaches.
Internacional
Si
JCR del ISI
Si
Título de la revista
Neurocomputing
ISSN
0925-2312
Factor de impacto JCR
1,634
Información de impacto
Datos JCR del año 2012
Volumen
135
DOI
Número de revista
Desde la página
45
Hasta la página
52
Mes
SIN MES
Ranking
Esta actividad pertenece a memorias de investigación
Participantes
  • Autor: A. Ibáñez
  • Autor: Maria Concepcion Bielza Lozoya (UPM)
  • Autor: P. Larrañaga
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Departamento: Inteligencia Artificial
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