Authors:
Fernanda Rodríguez
;
Federico Lecumberry
and
Alicia Fernández
Affiliation:
Facultad de Ingeniería and Universidad de la República, Uruguay
Keyword(s):
Electricity Fraud, Support Vector Machine, Optimum Path Forest, Unbalance Class Problem, Combining
Classifier, UTE.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Economics, Business and Forecasting Applications
;
Pattern Recognition
Abstract:
Non-technical losses detection is a complex task, with high economic impact. The diversity and big number
of consumption records, makes it very important to find an efficient automatic method for detection the largest
number of frauds with the least amount of experts’ hours involved in preprocessing and inspections. This
article analyzes the performance of a strategy based on learning from expert labeling: suspect/no-suspect,
with one using inspection labels: fraud/no-fraud. Results show that the proposed framework, suitable for
imbalance problems, improves performance in terms of the Fmeasure with inspection labels, avoiding hours of
experts labeling.