Mobile Advertizing

In this research, we make use of location data seen in Real-Time Bidding (RTB) environments. We have proposed and empirically evaluated a new social-targeting design for effective, privacy-friendly mobile advertising.

Social Network Analysis

In recent years, vast amounts of networked data on a broad range of information flows between interlinked entities have become available, such as calls and text messages linking telephone accounts or online users, and money transfers connecting bank accounts. In this research we develop and apply new techniques that leverage such networked data to make better prediction models in marketing and finance domains.

Mining Behavioral Big Data

The identification and comprehension of customers’ behavior is of crucial importance in current business environments. Mining huge transactional data can provide interesting insights, in domains as response modeling and fraud detection.

Explaining Documents' Classification

Document classification has widespread applications, such as with web pages for advertising, emails for legal discovery etc. Unfortunately, due to the high dimensionality, understanding the decisions made by the document classifiers is difficult using traditional techniques.

Rule extraction

The lack of transparency of many state-of-the-art data mining techniques renders them useless in any domain where comprehensibility is of importance. Rule extraction is a techinque designed to remedy this.

Bigraph Social Networks

Many real-world large datasets correspond to bipartite graph (bigraph) datasets, think for example of users rating movies or people visiting locations. We propose a three-stage framework for node classification within bigraphs, where relational learners are applied to the weighted unigraph projection.

Credit Scoring
Credit risk is the risk that a borrower does not honour its obligation to service debt which can occur when the debt is not serviced on time and/or in full. The predicted estimates of credit risk are used by banks to set the loan granting policy or to set capital requirements.

Swarm Intelligence for Data Mining

This research is at the intersection of two fascinating and increasingly popular domains: swarm intelligence and data mining. Whereas data mining has been a popular academic topic for decades, swarm intelligence is a relatively new sub-field of artificial intelligence which studies the emergent collective intelligence of groups of simple agents.

Text mining for politics

Research on sentiment analysis has a wide variety of economical and social applications, from finding out which books your friends will like or predicting the stock market to aiding in detecting depressed teenagers on social networking sites. Here we apply it to the political context of Belgium.

Customs Fraud Detection


Explaining Deep Learning Models

Deep learning has been shown to outperform many other prediction techniques in making accurate predictions using behavioral big data. Combining behavioral data and deep learning unfortunately results in incomprehensible black box predictions, which leads to skepticism to use it in practice. Explainable AI has become a research area that gained a lot of attention because of its implications on model deployment. Here, we aim at finding global and instance-based explanations that increase transparency when using such models.