@Article{IJNAMB-5-238,
author = {VINICIUS ALMENDRA AND DENIS EN  ̆ACHESCU},
title = {Using Self-Organizing Maps for Binary Classification with Highly Imbalanced Datasets},
journal = {International Journal of Numerical Analysis Modeling Series B},
year = {2014},
volume = {5},
number = {3},
pages = {238--254},
abstract = {Highly imbalanced datasets occur in domains like fraud detection, fraud prediction, and clinical diagnosis of rare diseases, among others. These datasets are characterized by the
existence of a prevalent class (e.g. legitimate sellers) while the other is relatively rare (e.g. fraudsters). 
Although small in proportion, the observations belonging to the minority class can be of a
crucial importance. In this work we extend an unsupervised learning technique-Self-Organizing
Maps-to use labeled data for binary classification under a constraint on the proportion of false
positives. The resulting technique was applied to two highly imbalanced real datasets, achieving
good results while being easier to interpret.},
issn = {},
doi = {https://doi.org/},
url = {http://global-sci.org/intro/article_detail/ijnamb/232.html}
}