David Martens

David Martens
University of Antwerp | UA · Department of Engineering Management

About

115
Publications
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5,058
Citations

Publications

Publications (115)
Article
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A key challenge in Artificial Intelligence (AI) has been the potential trade-off between the accuracy and comprehensibility of machine learning models, as that also relates to their safe and trusted adoption. While there has been a lot of talk about this trade-off, there is no systematic study that assesses to what extent it exists, how often it oc...
Preprint
Full-text available
This paper studies how counterfactual explanations can be used to assess the fairness of a model. Using machine learning for high-stakes decisions is a threat to fairness as these models can amplify bias present in the dataset, and there is no consensus on a universal metric to detect this. The appropriate metric and method to tackle the bias in a...
Article
Full-text available
The complexity of state-of-the-art modeling techniques for image classification impedes the ability to explain model predictions in an interpretable way. A counterfactual explanation highlights the parts of an image which, when removed, would change the predicted class. Both legal scholars and data scientists are increasingly turning to counterfact...
Article
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Every step we take in the digital world leaves behind a record of our behavior; a digital footprint. Research has suggested that algorithms can translate these digital footprints into accurate estimates of psychological characteristics, including personality traits, mental health or intelligence. The mechanisms by which AI generates these insights,...
Article
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“Lifestyle politics” suggests that political and ideological opinions are strongly connected to our consumption choices, music and food taste, cultural preferences, and other aspects of our daily lives. With the growing political polarization this idea has become all the more relevant to a wide range of social scientists. Empirical research in this...
Preprint
Full-text available
Every step we take in the digital world leaves behind a record of our behavior; a digital footprint. Research has suggested that algorithms can translate these digital footprints into accurate estimates of psychological characteristics, including personality traits, mental health or intelligence. The mechanisms by which AI generates these insights,...
Article
Full-text available
Party competition in Western Europe is increasingly focused on “issue competition”, which is the selective emphasis on issues by parties. The aim of this paper is to contribute methodologically to the increasing number of studies that deal with different aspects of parties’ issue competition and communication. We systematically compare the value an...
Article
Full-text available
Counterfactual explanations are viewed as an effective way to explain machine learning predictions. This interest is reflected by a relatively young literature with already dozens of algorithms aiming to generate such explanations. These algorithms are focused on finding how features can be modified to change the output classification. However, thi...
Preprint
Full-text available
Explainability is becoming an important requirement for organizations that make use of automated decision-making due to regulatory initiatives and a shift in public awareness. Various and significantly different algorithmic methods to provide this explainability have been introduced in the field, but the existing literature in the machine learning...
Preprint
Full-text available
Explaining firm decisions made by algorithms in customer-facing applications is increasingly required by regulators and expected by customers. While the emerging field of Explainable Artificial Intelligence (XAI) has mainly focused on developing algorithms that generate such explanations, there has not yet been sufficient consideration of customers...
Article
Social media networks have revolutionized social science research. Yet, a lack of comparative empirical analysis of these networks leave social scientists with little knowledge on the role that contextual factors play in the formation of social relations. In this paper we perform a large-scale comparison of parliamentary Twitter networks in 12 coun...
Article
Full-text available
Machine learning models built on behavioral and textual data can result in highly accurate prediction models, but are often very difficult to interpret. Linear models require investigating thousands of coefficients, while the opaqueness of nonlinear models makes things worse. Rule-extraction techniques have been proposed to combine the desired pred...
Preprint
In this paper we suggest NICE: a new algorithm to generate counterfactual explanations for heterogeneous tabular data. The design of our algorithm specifically takes into account algorithmic requirements that often emerge in real-life deployments: the ability to provide an explanation for all predictions, being efficient in run-time, and being able...
Article
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Many real-world large datasets correspond to bipartite graph data settings—think for example of users rating movies or people visiting locations. Although there has been some prior work on data analysis with such bigraphs, no general network-oriented methodology has been proposed yet to perform node classification. In this paper we propose a three-...
Article
Full-text available
Party competition in Western Europe is increasingly focused on “issue competition”, which is the selective emphasis on issues by parties. The aim of this paper is to contribute methodologically to the increasing number of studies that deal with different aspects of parties’ issue competition and communication. We systematically compare the value an...
Article
Full-text available
Predictive systems based on high-dimensional behavioral and textual data have serious comprehensibility and transparency issues: linear models require investigating thousands of coefficients, while the opaqueness of nonlinear models makes things worse. Counterfactual explanations are becoming increasingly popular for generating insight into model p...
Preprint
Machine learning using behavioral and text data can result in highly accurate prediction models, but these are often very difficult to interpret. Linear models require investigating thousands of coefficients, while the opaqueness of nonlinear models makes things even worse. Rule-extraction techniques have been proposed to combine the desired predic...
Article
Full-text available
The predictive power of increasingly common large-scale, behavioral data has been demonstrated by previous research. Such data capture human behavior through the actions and/or interactions of people. Their sparsity and ultra-high dimensionality pose significant challenges to state-of-the-art classification techniques. Moreover, no prior work has s...
Preprint
Full-text available
We study the interpretability of predictive systems that use high-dimensonal behavioral and textual data. Examples include predicting product interest based on online browsing data and detecting spam emails or objectionable web content. Recently, counterfactual explanations have been proposed for generating insight into model predictions, which foc...
Article
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The outstanding performance of deep learning (DL) for computer vision and natural language processing has fueled increased interest in applying these algorithms more broadly in both research and practice. This study investigates the application of DL techniques to classification of large sparse behavioral data-which has become ubiquitous in the age...
Article
In this customs fraud detection application, we analyse a unique data set of 9,624,124 records resulting from a collaboration with the Belgian customs administration. They are faced with increasing levels of international trade, which pressurizes regulatory control. Governments therefore rely on data mining to focus their limited resources on the m...
Article
The tax fraud detection domain is characterized by very few labelled data (known fraud/legal cases) that are not representative for the population due to sample selection bias. We use unsupervised anomaly detection (AD) techniques, which are uncommon in tax fraud detection research, to deal with these domain issues. We analyse a unique dataset cont...
Preprint
Full-text available
Record companies invest billions of dollars in new talent around the globe each year. Gaining insight into what actually makes a hit song would provide tremendous benefits for the music industry. In this research we tackle this question by focussing on the dance hit song classification problem. A database of dance hit songs from 1985 until 2013 is...
Article
Banks are continuously looking for novel ways to leverage their existing data assets. A major source of data that has not yet been used to the full extent is massive fine‐grained payment data on the bank's customers. In the paper, a design is proposed that builds predictive credit scoring models by using the fine‐grained payment data. Using a real...
Article
An important aspect of the growing e‐commerce sector involves the delivery of tangible goods to the end customer, the so‐called last mile. This final stage of the logistics chain remains highly inefficient due to the problem of failed deliveries. To address this problem, delivery service providers can apply data science to determine the optimal, cu...
Article
Full-text available
This paper introduces a new event model appropriate for classifying (binary) data generated by a “destructive choice” process, such as certain human behavior. In such a process, making a choice removes that choice from future consideration yet does not influence the relative probability of other choices in the choice set. The proposed Wallenius eve...
Article
Microfinance has known a large increase in popularity, yet the scoring of such credit still remains a difficult challenge. Credit scoring traditionally uses socio-demographic and credit data, which we complement in an innovative manner with data from Facebook. A distinction is made between the relationships that the available data imply: (1) LALs a...
Article
This study proposes a novel methodology, mixture-amount modeling (MAM), to investigate cross-media advertising synergy based on consumers’ media usage. MAM allows to derive optimal media mixes that can be different for different types of media users. The authors provide a proof of concept by analyzing 46,852 responses to 92 beauty care advertising...
Article
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Recent years have witnessed a growing number of publications dealing with the imbalanced learning issue. While a plethora of techniques have been investigated on traditional low-dimensional data, little is known on the effect thereof on behaviour data. This kind of data reflects fine-grained behaviours of individuals or organisations and is charact...
Article
Full-text available
We propose a Hawkish-Dovish (HD) indicator that measures the degree of 'hawkishness' or 'dovishness' of the media's perception of the ECB's tone at each press conference. We compare two methods to calculate the indicator: semantic orientation and Support Vector Machines text classification. We show that the latter method tends to provide more stabl...
Article
Bankruptcy prediction has been a popular and challenging research area for decades. Most prediction models are built using financial figures, stock market data and firm specific variables. We complement such traditional low-dimensional data with high-dimensional data on the company’s directors and managers in the prediction models. This information...
Article
In many real world applications classification models are required to be in line with domain knowledge and to respect monotone relations between predictor variables and the target class, in order to be acceptable for implementation. This paper presents a novel heuristic approach, called RULEM, to induce monotone ordinal rule based classification mo...
Article
Recently, the literature has measured economic policy uncertainty using news references, resulting in the frequently-mentioned ‘Economic Policy Uncertainty index’ (EPU). In the original setup, a news article is assumed to address policy uncertainty if it contains certain predefined keywords. We argue that the original setup is prone to measurement...
Article
Predictive modeling applications increasingly use data representing people's behavior, opinions, and interactions. Fine-grained behavior data often has different structure from traditional data, being very high-dimensional and sparse. Models built from these data are quite difficult to interpret, since they contain many thousands or even many milli...
Chapter
Full-text available
The task of recognizing a composer by listening to a musical piece used to be reserved for experts in music theory. The problem we address here is that of constructing an automatic system that is able to distinguish between music written by different composers and identifying the musical properties that are important for this task. We take a data-d...
Article
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In this article a number of musical features are extracted from a large musical database and these were subsequently used to build four composer-classification models. The first two models, an if–then rule set and a decision tree, result in an understanding of stylistic differences between Bach, Haydn, and Beethoven. The other two models, a logisti...
Article
This paper focuses on finding the same and similar users based on location-visitation data in a mobile environment. We propose a new design that uses consumer-location data from mobile devices (smartphones, smart pads, laptops, etc.) to build a "geosimilarity network" among users. The geosimilarity network (GSN) could be used for a variety of analy...
Article
Customer loyalty programs are largely present in the private sector and have been elaborately studied. Applications from the private sector have found resonance in a public setting, however, simply extrapolating research results is not acceptable, as their rationale inherently differs. This study focuses on data from a loyalty program issued by the...
Article
Many of the state-of-the-art data mining techniques introduce nonlinearities in their models to cope with complex data relationships effectively. Although such techniques are consistently included among the top classification techniques in terms of predictive power, their lack of transparency renders them useless in any domain where comprehensibili...
Article
Baig et al. [1] proposed a new classification rule mining algorithm in IEEE TEC. The technique introduces a correlation-based function for the ant colony optimization (ACO) component. The authors compare variants of the technique with several existing ACO-based algorithms and the state-of-the art RIPPER [2] algorithm. The AntMiner+ algorithm, propo...
Article
Full-text available
With the globalisation of the world's economies and ever-evolving financial structures, fraud has become one of the main dissipaters of government wealth and perhaps even a major contributor in the slowing down of economies in general. Although corporate residence fraud is known to be a major factor, data availability and high sensitivity have caus...
Article
Full-text available
Record companies invest billions of dollars in new talent around the globe each year. Gaining insight into what actually makes a hit song would provide tremendous benefits for the music industry. In this research we tackle this question by focussing on the dance hit song classification problem. A database of dance hit songs from 1985 until 2013 is...
Article
Full-text available
Despite the fact that both the Efficient Market Hypothesis and Random Walk Theory postulate that it is impossible to predict future stock prices based on currently available information, recent advances in empirical research have been proving the opposite by achieving what seems to be better than random prediction performance. We discuss some of th...
Article
Many document classification applications require human understanding of the reasons for data-driven classification decisions by managers, client-facing employees, and the technical team. Predictive models treat documents as data to be classified, and document data are characterized by very high dimensionality, often with tens of thousands to milli...
Article
This study examines the use of social network information for customer churn prediction. An alternative modeling approach using relational learning algorithms is developed to incorporate social network effects within a customer churn prediction setting, in order to handle large scale networks, a time dependent class label, and a skewed class distri...
Article
On the basis of two data sets containing Loss Given Default (LGD) observations of home equity and corporate loans, we consider non-linear and non-parametric techniques to model and forecast LGD. These techniques include non-linear Support Vector Regression (SVR), a regression tree, a transformed linear model and a two-stage model combining a linear...
Article
Full-text available
With the increasingly widespread collection and processing of “big data,” there is natural interest in using these data assets to improve decision making. One of the best understood ways to use data to improve decision making is via predictive analytics. An important, open question is: to what extent do larger data actually lead to better predictiv...
Article
Modeling credit rating migrations conditional on macroeconomic conditions allows financial institutions to assess, analyze, and manage the risk related to a credit portfolio. Existing methodologies to model credit rating migrations conditional on the business cycle suffer from poor accuracy, difficult readability, or model inconsistencies. The mode...
Article
Full-text available
While many papers propose innovative methods for constructing individual rules in separate-and-conquer rule learning algorithms, comparatively few study the heuristic rule evaluation functions used in these algorithms to ensure that the selected rules combine into a good rule set. Underestimating the impact of this component has led to suboptimal d...
Article
Advances in data mining have led to algorithms that produce accurate regression models for large and difficult to approximate data. Many of these use non-linear models to handle complex data-relationships in the input data. Their lack of transparency, however, is problematic since comprehensibility is a key requirement in many potential application...
Conference Paper
We present the work in progress on a rule mining algorithm for regression using particle swarm optimization (PSO). Sub problems occuring during development involve the encoding of rules as particles and suitable PSO parameter tuning. A key subtask is the selection of a good rule learning heuristic. We introduce a novel heuristic for which prelimina...
Article
Full-text available
At the year end of 2011 Belgium formed a government, after a world record breaking period of 541 days of negotiations. We have gathered and analysed 68; 000 related on-line news articles published in 2011 in Flemish newspapers. These articles were analysed by a custom-built expert system. The results of our text mining analyses show interesting die...
Article
Full-text available
Customer churn prediction models aim to indicate the customers with the highest propensity to attrite, allowing to improve the efficiency of customer retention campaigns and to reduce the costs associated with churn. Although cost reduction is their prime objective, churn prediction models are typically evaluated using statistically based performan...
Article
In this chapter we describe how comprehensible rules can be extracted from artificial neural networks (ANN) and support vector machines (SVM). ANN and SVM are two very popular techniques for pattern classification. In the business intelligence application domain of credit scoring, they have been shown to be effective tools for distinguishing betwee...
Article
Full-text available
The introduction of the Basel II Accord has had a huge impact on financial institutions, allowing them to build credit risk models for three key risk parameters: PD (probability of default), LGD (loss given default) and EAD (exposure at default). Until recently, credit risk research has focused largely on the estimation and validation of the PD par...
Article
For a complex industrial system, its multivariable and nonlinear nature generally make it very difficult, if not impossible, to obtain an accurate model, especially when the model structure is unknown. The control of this class of complex systems is ...
Article
This paper proposes a complete framework to assess the overall performance of classification models from a user perspective in terms of accuracy, comprehensibility, and justifiability. A review is provided of accuracy and comprehensibility measures, and a novel metric is introduced that allows one to measure the justifiability of classification mod...
Article
This design science paper presents a method for targeting consumers based on a 'pseudo-social network' (PSN): consumers are linked if they transfer money to the same entities. A marketer can target those individuals that are strongly connected to key individuals. We present the PSN design and a large-scale empirical study using data from a major ba...