Anzeige
Erschienen in:
2018 | OriginalPaper | Buchkapitel
Embeddings of Categorical Variables for Sequential Data in Fraud Context
verfasst von : Yoan Russac, Olivier Caelen, Liyun He-Guelton
Erschienen in: The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018)
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Abstract
In this paper we propose a new generic method to work with categorical variables in case of sequential data. Our main contributions are: (1) The use of unsupervised methods to extract sequential information, (2) The generation of embeddings including this sequential information for categorical variables using the well-known Word2Vec neural network. The use of embeddings not only reduced the memory usage but also improved the machine learning algorithms learning capacity from data compared with commonly used One-Hot encoding. We implemented those processes on a real world credit card fraud dataset, which represents more than 400 million transactions over a one year time window. We demonstrated that we were able to reduce the memory usage by 50% and to improve performance by 3% points while using only a small subset of features.