Embeddings of Categorical Variables for Sequential Data in Fraud Context


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 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 for example. 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% while using only a small subset of features.

In International Conference on Advanced Machine Learning Technologies and Applications 2018
Yoan Russac
Chercheur en Machine Learning

Je suis intéressé par le Machine Learning et plus particulièrement les méthodes d’apprentissage séquentiel.