id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_gr5hjfqjdrfy5oniy6sunevkny	L. Cleofas-Sánchez	Associative learning on imbalanced environments: An empirical study	2016	28	.pdf	application/pdf	9479	1252	76	the applicability of the associative neural networks to the classification of imbalanced data. methods at the algorithmic level modify the existing learning models for biasing the discrimination process towards the minority class; the data level solutions • To explore the performance of the hybrid associative memory with translation and compare this against other popular classification methods of different nature; 2013; López et al., 2012, 2013; Yu et al., 2013, 2015), which consists of generating skewed data sets with different levels of class imbalance. is imbalanced; (ii) to find out whether there exist differences in its behavior depending on the imbalance ratio; and (iii) to determine how the application of resampling strategies affects the effectiveness of the HACT classifier. over-sampling algorithms degrade the effectiveness of the HACT classifier, especially with the SMOTE method (RBF, SVM and NNge perform significantly A kernel-based two-class classifier for imbalanced data sets.	./cache/work_gr5hjfqjdrfy5oniy6sunevkny.pdf	./txt/work_gr5hjfqjdrfy5oniy6sunevkny.txt