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Seppe KLM vanden Broucke

Seppe KLM vanden Broucke
Ghent University | UGhent · Department of Management Information and Operations Management

PhD in Applied Economics

About

108
Publications
23,785
Reads
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1,295
Citations
Citations since 2016
80 Research Items
1197 Citations
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Introduction
Seppe vanden Broucke received a PhD in Applied Economics at KU Leuven, Belgium in 2014. Currently, Seppe is working as an assistant professor at the department of Business Informatics at UGent (Belgium) and is a lecturer at KU Leuven (Belgium). Seppe's research interests include business data mining and analytics, machine learning, process management, process mining. His work has been published in well-known international journals and presented at top conferences.
Additional affiliations
January 2011 - January 2016
KU Leuven
Position
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Publications

Publications (108)
Article
Full-text available
Microblogging websites such as Twitter have caused sentiment analysis research to increase in popularity over the last several decades. However, most studies focus on the English language, which leaves other languages underrepresented. Therefore, in this paper, we compare several modeling techniques for sentiment analysis using a new dataset contai...
Article
Full-text available
Class imbalance is a critical issue in customer classification, for which a plethora of techniques have been proposed in the current body of literature. In particular, generative adversarial network (GAN)-based oversampling can capture the true data distribution of minority class samples and generate new samples, and this approach has demonstrated...
Chapter
Currently, the state of a house is typically assessed by an expert, which is time and resource intensive. Therefore, an automatic assessment could have economic, social and ecological benefits. Hence, this study presents a binary classification model using transfer learning to classify Google Street View images of houses. For this purpose, a three-...
Chapter
Full-text available
Predictive process monitoring concerns itself with the prediction of ongoing cases in (business) processes. Prediction tasks typically focus on remaining time, outcome, next event or full case suffix prediction. Various methods using machine and deep learning have been proposed for these tasks in recent years. Especially recurrent neural networks (...
Preprint
Full-text available
Within the field of process mining, several different trace clustering approaches exist for partitioning traces or process instances into similar groups. Typically, this partitioning is based on certain patterns or similarity between the traces, or driven by the discovery of a process model for each cluster. The main drawback of these techniques, h...
Preprint
Full-text available
Predictive process monitoring concerns itself with the prediction of ongoing cases in (business) processes. Prediction tasks typically focus on remaining time, outcome, next event or full case suffix prediction. Various methods using machine and deep learning have been proposed for these tasks in recent years. Especially recurrent neural networks (...
Article
In order to improve the performance of any machine learning model, it is important to focus more on the data itself instead of continuously developing new algorithms. This is exactly the aim of feature engineering. It can be defined as the clever engineering of data hereby exploiting the intrinsic bias of the machine learning technique to our benef...
Article
Full-text available
Within the field of process mining, several different trace clustering approaches exist for partitioning traces or process instances into similar groups. Typically, this partitioning is based on certain patterns or similarity between the traces, or driven by the discovery of a process model for each cluster. The main drawback of these techniques, h...
Chapter
Full-text available
Conformance checking is concerned with the task of assessing the quality of process models describing actual behavior captured in an event log across different dimensions. In this paper, a novel approach for obtaining the degree of recall and precision between a process model and event log is introduced. The approach relies on the generation of a s...
Article
Developing accurate analytical credit scoring models has become a major focus for financial institutions. For this purpose, numerous classification algorithms have been proposed for credit scoring. However, the application of deep learning algorithms for classification has been largely ignored in the credit scoring literature. The main motivation f...
Chapter
Full-text available
Conformance checking describes process mining techniques used to compare an event log and a corresponding process model. In this paper, we propose an entirely new approach to conformance checking based on neural network-based embeddings. These embeddings are vector representations of every activity/task present in the model and log, obtained via ac...
Article
Full-text available
Calibration is a technique used to obtain accurate probability estimation for classification problems in real applications. Class imbalance can create considerable challenges in obtaining accurate probabilities for calibration methods. However, previous research has paid little attention to this issue. In this paper, we present an experimental inve...
Chapter
Representation Learning in dynamic networks has gained increasingly more attention due to its promising applicability. In the literature, we can find two popular approaches that have been adapted to dynamic networks: random-walk based techniques and graph-autoencoders. Despite the popularity, no work has compared them in well-know datasets. We fill...
Article
Full-text available
Separating decision modelling from the processes modelling concern recently gained significant support in literature, as incorporating both concerns into a single model impairs the scalability, maintainability, flexibility and understandability of both processes and decisions. Most notably the introduction of the Decision Model and Notation (DMN) s...
Article
Full-text available
Generating insights and value from data has become an important asset for organizations. At the same time, the need for experts in analytics is increasing and the number of analytics applications is growing. Recently, a new trend has emerged, i.e. analytics-as-a-service platforms, that makes it easier to apply analytics both for novice and expert u...
Technical Report
Full-text available
This report illustrates the interactions between decisions and processes in a real-life enriched event log revolving around a bank loan application and approval process. The decision models are represented using the recently introduced Decision Model and Notation (DMN) standard of the Object Management Group (OMG). For this purpose, we capitalise o...
Article
Full-text available
Imbalanced classification is a challenging issue in data mining and machine learning, for which a large number of solutions have been proposed. In this paper, we introduce an R library called IRIC, which integrates a wide set of solutions for imbalanced binary classification. IRIC not only provides a new implementation of some state-of-art techniqu...
Article
Full-text available
The analysis of business processes is a multifaceted problem that is comprised of analysing both activities’ workflow, as well as the decisions that are made throughout that workflow. In process mining, the automated discovery of process models from event data, a strong emphasis can be found towards discovering this workflow, as well as how data in...
Chapter
The research area of process mining concerns itself with knowledge discovery from event logs, containing recorded traces of executions as stored by process aware information systems. Over the past decade, research in process mining has increasingly focused on predictive process monitoring to provide businesses with valuable information in order to...
Article
The aspect of collaboration is gaining a considerable amount of importance in current logistics operations. The large number of dynamics that arise in collaborative logistics processes with numerous complexities and variations can make the modelling of such collaborative logistics processes a challenging task. Hence, a systematic modelling approach...
Book
Cambridge Core - Knowledge Management, Databases and Data Mining - Principles of Database Management - by Wilfried Lemahieu
Article
When content consumers explicitly judge content positively, we consider them to be engaged. Unfortunately, explicit user evaluations are difficult to collect, as they require user effort. Therefore, we propose to use device interactions as implicit feedback to detect engagement. We assess the usefulness of swipe interactions on tablets for predicti...
Chapter
Up until now, we’ve been focusing a lot on the “web scraping” part of this book. We now take a step back and link the concepts you’ve learned to the general field of data science, paying particular attention to managerial issues that will arise when you’re planning to incorporate web scraping in a data science project. This chapter also provides a...
Chapter
Together with HTML and CSS, JavaScript forms the third and final core building block of the modern web. We’ve already seen JavaScript appearing occasionally throughout this book, and it’s time that we take a closer look at it. As we’ll soon see in this chapter, our sturdy requests plus Beautiful Soup combination is no longer a viable approach to sc...
Chapter
We’ve already seen most of the core building blocks that make up the modern web: HTTP, HTML, and CSS. However, we’re not completely finished with HTTP yet. So far, we’ve only been using one of HTTP’s request “verbs” or “methods”: “GET”. This chapter will introduce you to the other methods HTTP provides, starting with the “POST” method that is commo...
Chapter
You’re now ready to get started with your own web scraping projects. This chapter wraps up by providing some closing topics. First, we provide an overview of other helpful tools and libraries you might wish to use in the context of web scraping, followed by a summary of best practices and tips to consider when web scraping.
Chapter
So far, the examples in the book have been quite simple in the sense that we only scraped (mostly) a single page. When writing web scrapers, however, there are many occasions where you’ll wish to scrape multiple pages and even multiple websites. In this context, the name “web crawler” is oftentimes used, as it will “crawl” across a site or even the...
Chapter
This chapter includes several larger examples of web scrapers. Contrary to most of the examples showcased during the previous chapters, the examples here serve a twofold purpose. First, they showcase some more examples using real-life websites instead of a curated, safe environment. The reason why we haven’t used many real-life examples so far is d...
Chapter
In this chapter, we introduce one of the core building blocks that makes up the web: the HyperText Transfer Protocol (HTTP), after having provided a brief introduction to computer networks in general. We then introduce the Python requests library, which we’ll use to perform HTTP requests and effectively start retrieving websites with Python. The ch...
Chapter
So far we have discussed the basics of HTTP and how you can perform HTTP requests in Python using the requests library. However, since most web pages are formatted using the Hypertext Markup Language (HTML), we need to understand how to extract information from such pages. As such, this chapter introduces you to HTML, as well as another core buildi...
Article
The development of new data analytical methods remains a crucial factor in the combat against insurance fraud. Methods rooted in the research field of anomaly detection are considered as promising candidates for this purpose. Commonly, a fraud data set contains both numeric and nominal attributes, where, due to the ease of expressiveness, the latte...
Book
This book provides a complete and modern guide to web scraping, using Python as the programming language, without glossing over important details or best practices. Written with a data science audience in mind, the book explores both scraping and the larger context of web technologies in which it operates, to ensure full understanding. The authors...
Article
To detect churners in a vast customer base, as is the case with telephone service providers, companies heavily rely on predictive churn models to remain competitive in a saturated market. In previous work, the expected maximum profit measure for customer churn (EMPC) has been proposed in order to determine the most profitable churn model. However,...
Article
Full-text available
Customer retention campaigns increasingly rely on predictive models to detect potential churners in a vast customer base. From the perspective of machine learning, the task of predicting customer churn can be presented as a binary classification problem. Using data on historic behavior, classification algorithms are built with the purpose of accura...
Conference Paper
Full-text available
The interest of integrating decision analysis approaches with the automated discovery of processes from data has seen a vast surge over the past few years. Most notably the introduction of the Decision Model and Notation (DMN) standard by the Object Management Group has provided a suitable solution for filling the void of decision representation in...
Article
Full-text available
The discovery of a formal process model from event logs describing real process executions is a challenging problem that has been studied from several angles. Most of the contributions consider the extraction of a model as a one-class supervised learning problem where only a set of process instances is available. Moreover, the majority of technique...
Conference Paper
Trace clustering techniques are a set of approaches for partitioning traces or process instances into similar groups. Typically, this partitioning is based on certain patterns or similarity between the traces, or done by discovering a process model for each cluster of traces. In general, however, it is likely that clustering solutions obtained by t...
Conference Paper
Full-text available
The term Decision Mining has been put forward in literature to cover numerous applications in a diverse set of contexts. In the business process management community, it typically reflects the way processes and data required for decision purposes in those processes are blended into one model during discovery. However, the upcoming field of decision...
Article
Full-text available
This paper presents a technique that aims to increase human understanding of trace clustering solutions. The clustering techniques under scrutiny stem from the process mining domain, where the clustering of process instances is deemed a useful technique to analyse process data with a large variety of behaviour. Until now, the most often used method...
Article
Class imbalance brings significant challenges to customer churn prediction. Many solutions have been developed to address this issue. In this paper, we comprehensively compare the performance of state-of-the-art techniques to deal with class imbalance in the context of churn prediction. A recently developed expected maximum profit criterion is used...
Article
In this paper, we present Fodina, a process discovery technique with a strong focus on robustness and flexibility. To do so, we improve upon and extend an existing process discovery algorithm, namely Heuristics Miner. We have identified several drawbacks which impact the reliability of existing heuristic-based process discovery techniques and there...