TY - JOUR
T1 - Using Self-Organizing Maps for Binary Classification with Highly Imbalanced Datasets
AU - VINICIUS ALMENDRA AND DENIS EN  ̆ACHESCU
JO - International Journal of Numerical Analysis Modeling Series B
VL - 3
SP - 238
EP - 254
PY - 2014
DA - 2014/05
SN - 5
DO - http://doi.org/
UR - https://global-sci.org/intro/article_detail/ijnamb/232.html
KW - unsupervised learning
KW - self-organizing maps
KW - imbalanced datasets
KW - supervised learning
AB - 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.