Working with Deep Generative Models and Tabular Data Imputation

Scientific Conference (2020, July 17)

Datasets with missing values are very common in industry applications. Missing data typically have a negative impact on machine learning models. With the rise of generative models in deep learning, recent ... [more ▼]

Datasets with missing values are very common in industry applications. Missing data typically have a negative impact on machine learning models. With the rise of generative models in deep learning, recent studies proposed solutions to the problem of imputing missing values based various deep generative models. Previous experiments with Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) showed promising results in this domain. Initially, these results focused on imputation in image data, e.g. filling missing patches in images. Recent proposals addressed missing values in tabular data. For these data, the case for deep generative models seems to be less clear. In the process of providing a fair comparison of proposed methods, we uncover several issues when assessing the status quo: the use of under-specified and ambiguous dataset names, the large range of parameters and hyper-parameters to tune for each method, and the use of different metrics and evaluation methods. [less ▲]

Improving Missing Data Imputation with Deep Generative Models

E-print/Working paper (2019)

GAN Applications with Discrete Data

Poster (2018, June 27)

Finding Suspicious Activities in Financial Transactions and Distributed Ledgers

in Proceedings of the 17th IEEE International Conference on Data Mining Workshops (ICDMW 2017) (2017)

Banks and financial institutions around the world must comply with several policies for the prevention of money laundering and in order to combat the financing of terrorism. Nowadays, there is a raise in ... [more ▼]

Banks and financial institutions around the world must comply with several policies for the prevention of money laundering and in order to combat the financing of terrorism. Nowadays, there is a raise in the popularity of novel financial technologies such as digital currencies, social trading platforms and distributed ledger payments, but there is a lack of approaches to enforce the aforementioned regulations accordingly. Software tools are developed to detect suspicious transactions usually based on knowledge from experts in the domain, but as new criminal tactics emerge, detection mechanisms must be updated. Suspicious activity examples are scarce or nonexistent, hindering the use of supervised machine learning methods. In this paper, we describe a methodology for analyzing financial information without the use of ground truth. A user suspicion ranking is generated in order to facilitate human expert validation using an ensemble of anomaly detection algorithms. We apply our procedure over two case studies: one related to bank fund movements from a private company and the other concerning Ripple network transactions. We illustrate how both examples share interesting similarities and that the resulting user ranking leads to suspicious findings, showing that anomaly detection is a must in both traditional and modern payment systems. [less ▲]