Associative learning on imbalanced environments: An empirical study
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Show full item recordcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7038
comunitat-uji-handle3:10234/8634
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Title
Associative learning on imbalanced environments: An empirical studyAuthor (s)
Date
2015-10xmlui.dri2xhtml.METS-1.0.item-edition
PreprintPublisher
ElsevierBibliographic citation
CLEOFAS-SÁNCHEZ, L., et al. Associative learning on imbalanced environments: An empirical study. Expert Systems with Applications, 2015.Type
info:eu-repo/semantics/articlePublisher version
http://www.sciencedirect.com/science/article/pii/S0957417415006880Version
info:eu-repo/semantics/publishedVersionSubject
Abstract
Associative memories have emerged as a powerful computational neural network model for several pattern classification problems. Like most traditional classifiers, these models assume that the classes share similar ... [+]
Associative memories have emerged as a powerful computational neural network model for several pattern classification problems. Like most traditional classifiers, these models assume that the classes share similar prior probabilities. However, in many real-life applications the ratios of prior probabilities between classes are extremely skewed. Although the literature has provided numerous studies that examine the performance degradation of renowned classifiers on different imbalanced scenarios, so far this effect has not been supported by a thorough empirical study in the context of associative memories. In this paper, we fix our attention on the applicability of the associative neural networks to the classification of imbalanced data. The key questions here addressed are whether these models perform better, the same or worse than other popular classifiers, how the level of imbalance affects their performance, and whether distinct resampling strategies produce a different impact on the associative memories. In order to answer these questions and gain further insight into the feasibility and efficiency of the associative memories, a large-scale experimental evaluation with 31 databases, seven classification models and four resampling algorithms is carried out here, along with a non-parametric statistical test to discover any significant differences between each pair of classifiers. [-]
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Expert Systems with Applications Volume 54, 15 July 2016Rights
info:eu-repo/semantics/openAccess
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