Koen Vanhoof

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• Full Professor
• Professor at Hasselt University

Decision-making could be defined as the process to choose a suitable decision among a set of possible alternatives in a given activity. In this field, Fuzzy Cognitive Maps (FCMs) have gained significant attention for their ability to model complex systems through causal relationships between concepts. While FCMs are transparent and adaptable models...

In this paper, we integrate the concepts of feature importance with implicit bias in the context of pattern classification. This is done by means of a three-step methodology that involves (i) building a classifier and tuning its hyperparameters, (ii) building a Fuzzy Cognitive Map model able to quantify implicit bias, and (iii) using the SHAP featu...

This paper presents a projection-based clustering method for visualizing high-dimensional data points in lower-dimensional spaces while preserving the data’s structural properties. The proposed method modifies the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm by adding a weight function that adjusts the dissimilarity between high-di...

Introducción: La clasificación multietiqueta es una variante de la clasificación tradicional de etiqueta única, en la que un objeto ya no se clasifica exclusivamente por una etiqueta. En su lugar, este aprendizaje pretende asignar a un objeto una o más clases de etiquetas de un conjunto predefinido de clases. Dado que el aprendizaje multietiqueta s...

Considering the good performance of the Interval-valued Long-term Cognitive Networks model and the Nonsynaptic Backpropagation learning variants proposed as training learning to the method. This article is focused on the application of both proposals in the analysis of the incidence of different factors on the level of service at intersections with...

This paper presents a projection-based clustering method for visualizing high-dimensional data points in lower-dimensional spaces while preserving the data’s structural properties. The proposed method modifies the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm by adding a weight function that adjusts the dissimilarity between high-di...

Empirical evidence suggests that algorithmic decisions driven by Machine Learning (ML) techniques threaten to discriminate against legally protected groups or create new sources of unfairness. This work supports the contextual approach to fairness in EU non-discrimination legal framework and aims at assessing up to what point we can assure legal fa...

In this paper, we integrate the concepts of feature importance with implicit bias in the context of pattern classification. This is done by means of a three-step methodology that involves (i) building a classifier and tuning its hyperparameters, (ii) building a Fuzzy Cognitive Map model able to quantify implicit bias, and (iii) using the SHAP featu...

The recently published Interval-valued Long-term Cognitive Networks have shown promising results when reasoning under uncertainty conditions. In these recurrent neural networks, the interval weights are learned using a nonsynaptic backpropagation learning algorithm. Similar to traditional propagation-based algorithms, this variant might suffer from...

Forecasting windmill time series is often the basis of other processes such as anomaly detection, health monitoring, or maintenance scheduling. The amount of data generated by windmill farms makes online learning the most viable strategy to follow. Such settings require retraining the model each time a new batch of data is available. However, updat...

Time series processing is an essential aspect of wind turbine health monitoring. In this paper, we propose two new approaches for analyzing wind turbine health. Both methods are based on abstract concepts, implemented using fuzzy sets, which allow aggregating and summarizing the underlying raw data in terms of relative low, moderate, and high power...

Predict customer buying behavior is an important task for improving direct marketing campaigns, offering the best possible experiences, and providing personalization in the customer journey trip. Improving how models capture the sequential information from transactional data is essential to learn customer buying order and repetitive buying patterns...

This article presents a comprehensive approach for time-series classification. The proposed model employs a fuzzy cognitive map (FCM) as a classification engine. Preprocessed input data feed the employed FCM. Map responses, after a postprocessing procedure, are used in the calculation of the final classification decision. The time-series data are s...

Time series processing is an essential aspect of wind turbine health monitoring. Despite the progress in this field, there is still room for new methods to improve modeling quality. In this paper, we propose two new approaches for the analysis of wind turbine health. Both approaches are based on abstract concepts, implemented using fuzzy sets, whic...

Time series similarity evaluation is a crucial processing task performed either as a stand-alone action or as a part of extensive data analysis schemes. Among essential procedures that rely on measuring time series similarity, we find time series clustering and classification. While the similarity of regular (not temporal) data frames is studied ex...

COVID-19 has been affected worldwide since the end of 2019. Clinical studies have shown that a factor that increases its lethality is the existence of secondary infections. Coinfections associated with the infection SARS-CoV-2 are classified into bacterial infections and fungal infections. A patient may develop one, both, or neither. From a machine...

Forecasting windmill time series is often the basis of other processes such as anomaly detection, health monitoring, or maintenance scheduling. The amount of data generated on windmill farms makes online learning the most viable strategy to follow. Such settings require retraining the model each time a new batch of data is available. However, updat...

Rough set theory is a granular computing formalism that allows analyzing a given dataset through well-defined measures. Some of these measures aim to characterize datasets used to discover knowledge, mostly in traditional classification problems. Measuring the data quality is pivotal to estimate beforehand the problem’s difficulty since a classific...

Decision Model and Notation (DMN) has become a relevant topic for organizations since it allows users to control their processes and organizational decisions. The increasing use of DMN decision tables to capture critical business knowledge raises the need for supporting analysis tasks such as the extraction of inputs, outputs and their relations fr...

Data reduction techniques play a key role in instance-based classification to lower the amount of data to be processed. Prototype generation aims to obtain a reduced training set in order to obtain accurate results with less effort. This translates into a significant reduction in both algorithms’ spatial and temporal burden. This issue is particula...

In many knowledge discovery applications, finding outliers, i.e. objects that behave in an unexpected way or have abnormal properties, is more interesting than finding inliers in a dataset. Outlier detection is important for many applications, including those related to intrusion detection, credit card fraud, and criminal activity in e-commerce. Se...

Understanding customer behaviors is deemed crucial to improve customers’ satisfaction and loyalty, which eventually is materialized in increased revenue. This paper tackles this challenge by using complex networks and multiple instance reasoning to examine the network structure of Customer Purchasing Behaviors. Our main contributions rely on a new...

Fuzzy Cognitive Maps (FCMs) are recurrent neural networks made up of well-defined neurons and causal relations. Fuzzy Grey Cognitive Maps (FGCMs) are an extension of FCMs, intended to surpass the intrinsic uncertainties modeling real-world problems by means of Grey theory. Despite the rising number of studies about FGCM-based models, little has bee...

Pooling layers help reduce redundancy and the number of parameters before building a multilayered neural network that performs the remaining processing operations. Usually, pooling operators in deep learning models use an explicit topological organization, which is not always possible to obtain on multi-label data. In a previous paper, we proposed...

Rough set theory has many interesting applications in circumstances characterized by vagueness. In this paper, the applications of rough set theory in community detection analysis are discussed based on the Rough Net definition. We will focus the application of Rough Net on community detection validity in both monoplex and multiplex networks. Also,...

Pooling layers help reduce redundancy and the number of parameters in deep neural networks without the need of performing additional learning processes. Although these operators are able to deal with both single-label and multi-label problems they are specifically aimed at reducing feature space. However, in the case of multi-label data, this shoul...

Pooling layers help reduce redundancy and the number of parameters before building a multilayered neural network that performs the remaining processing operations. Usually, pooling operators in deep learning models use an explicit topological organization, which is not always possible to define on multi-label data. Some authors have addressed this...

Fuzzy Cognitive Maps (FCMs) are recurrent neural networks made up of well-defined neurons and causal relations. Fuzzy Grey Cognitive Maps (FGCMs) are an extension of FCMs, intended to surpass the intrinsic uncertainties modeling real-world problems by means of Grey theory. Despite the rising number of studies about FGCM-based models , little has be...

Forecasting multivariate time series is an important problem considered in many real-world scenarios. To deal with that problem, several forecasting models have already been proposed, where Fuzzy Cognitive Maps (FCMs) are proved to be a suitable alternative. The key limitation of the existing FCM-based forecasting models is the lack of time-efficie...

Fuzzy Cognitive Maps (FCMs) are recurrent neural networks comprised of well-defined concepts and causal relations. While the literature about real-world FCM applications is prolific, the studies devoted to understanding the foundations behind these neural networks are rather scant. In this paper, we introduce several definitions and theorems that u...

Hybrid artificial intelligence deals with the construction of intelligent systems by relying on both human knowledge and historical data records. In this paper, we approach this problem from a neural perspective, particularly when modeling and simulating dynamic systems. Firstly, we propose a Fuzzy Cognitive Map architecture in which experts are re...

All over the world some people and companies try to avoid taxes whenever possible. Customs are not an exception to this. In this paper we investigate how customs fraud can be detected using data mining on logistics transaction data. We used both the Apriori algorithm for association rules and decision tree analysis to do so. We first transformed th...

Logistics companies possess and collect a large amount of data on the shipments they perform while at the same time facing a challenge to understand their complicated market better. Therefore, investigating whether large databases gathered by logistics companies on their e-commerce partners could be monetised as a business service and how this coul...

Pattern classification is a popular research field within the Machine Learning discipline. Black-box models have proven to be potent classifiers in this particular field. However, their inability to provide a transparent decision mechanism is often regarded as an undesirable feature. Fuzzy-Rough Cognitive Networks are granular classifiers that have...

Data reduction techniques play a key role in instance-based classification to lower the amount of data to be processed. Prototype generation aims to obtain a reduced training set in order to obtain accurate results with less effort. This translates into a significant reduction in both algorithms’ spatial and temporal burden. This issue is particula...

Within the neural computing field, Fuzzy Cognitive Maps (FCMs) are attractive simulation tools to model dynamic systems by means of well-defined neural concepts and causal relationships, thus equipping the network with interpretability features. However, such components are normally described by quantitative terms, which may be difficult to handle...

Fuzzy cognitive maps (FCMs) keep growing in popularity within the scientific community. However, despite substantial advances in the theory and applications of FCMs, there is a lack of an up-to-date, comprehensive presentation of the state-of-the-art in this domain. In this review study we are filling that gap. First, we present basic FCM concepts...

Within the neural computing field, Fuzzy Cognitive Maps (FCMs) are attractive simulation tools to model dynamic systems by means of well-defined neural concepts and causal relationships, thus equipping the network with interpretability features. However, such components are normally described by quantitative terms, which may be difficult to handle...

Logistics companies possess collect large amount of data on the shipments they perform while at the same time facing a challenge to understand their complicated market better. They can extract useful market knowledge by using data mining technologies such as visualization and clustering. The detailed results of such big data analytics methods can a...

In contrast with the extensive variety of machine learning algorithms, to fully automate the reasoning process, only a few can take advantage of the expert knowledge. Fuzzy Cognitive Maps (FCMs) are neural networks that can naturally integrate this kind of knowledge in the inference process. Nevertheless, FCMs have serious drawbacks difficult to ov...

In this paper, we survey different Granular Computing (GrC) applications to the field of cognitive mapping by highlighting how Fuzzy Cognitive Maps (FCMs) have been augmented with different types of information granules such as intervals, fuzzy sets, fuzzy clustering, rough sets and grey sets. These information granules have been integrated into co...

In the last years, the amounts of data have increased considerably and therefore, it is becoming more complex to handle these volumes of information. Measuring the data quality is a pivotal aspect to assess the classifier’s discriminatory power as the classifiers accuracy heavily depends on the data used to build the model. Multi-label classification...

In multi-label classification problems, instances can be associated with several decision classes (labels) simultaneously. One of the most successful algorithms to deal with this kind of problem is the ML-kNN method, which is lazy learner adapted to the multi-label scenario. All the computational models that realize inferences from examples have th...

The efficiency of e-mail campaigns is a big challenge for any e-commerce venture in terms of the response rate of e-mail campaigns and customer segmentation based on loyalty. Decision tree analysis are useful tools to extract customer information related to response rate from e-mail campaigns data. This study aims at predicting customer loyalty and...

Modeling a real-world system by means of a neural model involves numerous challenges that range from formulating transparent knowledge representations to obtaining reliable simulation errors. However, that knowledge is often difficult to formalize in a precise way using crisp numbers. In this paper, we present the long-term grey cognitive networks...

We introduce a neural cognitive mapping technique named Long-Term Cognitive Network (LTCN) that is able to memorize long-term dependencies between a sequence of input and output vectors, especially in those scenarios that require predicting the values of multiple dependent variables at the same time. The proposed technique is an extension of a rece...

While the machine learning literature dedicated to fully automated reasoning algorithms is abundant, the number of methods enabling the inference process on the basis of previously defined knowledge structures is scanter. Fuzzy Cognitive Maps (FCMs) are recurrent neural networks that can be exploited towards this goal because of their flexibility t...

Fuzzy Cognitive Maps (FCMs) can be defined as recurrent neural networks that allow modeling complex systems using concepts and causal relations. While this Soft Computing technique has proven to be a valuable knowledge-based tool for building Decision Support Systems, further improvements related to its transparency are still required. In this pape...

Rough Cognitive Ensembles (RCEs) can be defined as a multiclassifier system composed of a set of Rough Cognitive Networks (RCNs), each operating at a different granularity degree. While this model is capable of outperforming several traditional classifiers reported in the literature, there is still room for enhancing its performance. In this paper,...

Fuzzy Cognitive Maps (FCMs) have become a suitable and proven knowledge-based methodology for systems modeling and simulation. This technique is especially attractive when modeling systems characterized by ambiguity, and/or non-trivial causalities among its variables. The rich literature that is found related to FCMs reports very clearly many succe...

Over the last decades, the field of process mining has emerged as a response to a growing amount of event data being recorded in the context of business processes. Concurrently with the increasing amount of literature produced in this field, a set of tools has been developed to implement the various algorithms and provide them to end users. However...

On-line companies usually maintain complex information systems for capturing records about Customer Purchasing Behaviors (CPBs) in a cost-effective manner. Building prediction models from this data is considered a crucial step of most Decision Support Systems used in business informatics. Segmentation of similar CPB is an example of such an analysi...

Multi-label classification refers to the problem of associating an object with multiple labels. This problem has been successfully addressed from the perspective of problem transformation and adaptation of algorithms. Multi-Label k-Nearest Neighbour (MLkNN) is a lazy learner that has reported excellent results, still there is room for improvements....

While the machine learning literature dedicated to fully automated reasoning algorithms is abundant, the number of methods enabling the inference process on the basis of previously defined knowledge structures is scanter. Fuzzy Cognitive Maps (FCMs) are neural networks that can be exploited towards this goal because of their flexibility to handle e...

In recent decades, activity-based transportation models have gained growing attention, due to their strong foundation in behavioral theory and ability to model the response of individuals to travel demand management policies. Hence, researchers have become increasingly interested in analyzing and predicting individuals’ decisions about activity par...

A recent trend in Machine Learning is to augment the transparency of traditional classification models using Granular Computing techniques. This approach has been found particularly useful in the neural networks field since most successful neural systems often require complex structures to behave like universal approximators. However, there is a wi...

In recent years, Fuzzy Cognitive Maps (FCMs) have become a convenient knowledge-based tool for economic modeling. Perhaps, the most attractive feature of these cognitive networks relies on their transparency when performing the reasoning process. For example, in the context of time series forecasting, an FCM-based model allows predicting the next o...

In this paper, we address some shortcomings of Fuzzy Cognitive Maps (FCMs) in the context of time series prediction. The transparent and comprehensive nature of FCMs provides several advantages that are appreciated for decision-maker. In spite of this fact, FCMs also have some features that are hard to match with time series prediction, resulting i...

Fuzzy Cognitive Maps (FCMs) have proven to be a suitable methodology for the design of knowledge-based systems. By combining both uncertainty depiction and cognitive mapping, this technique represents the knowledge of systems that are characterized by ambiguity and complexity. In short, FCMs can be defined as recurrent neural networks that include...

Roughly speaking, decision-making can be defined as the process to select a decision (or group of decisions) among a set of possible alternatives in a given decision activity. Most real-life problems are unstructured in nature, often involving vagueness and uncertainty. This makes difficult to apply exact models, being necessary to use approximate...

A pivotal difference between Artificial Neural Networks and Fuzzy Cognitive Maps (FCMs) is that the latter allow modeling a physical system in terms of concepts and causal relations, thus equipping the network with interpretability features. However, such components are normally described by quantitative terms, which may be difficult to handle by d...

After 30 years of research, challenges and solutions, Fuzzy Cognitive Maps (FCMs) have become a suitable knowledge-based methodology for modeling and simulation. This technique is especially attractive when modeling systems that are characterized by ambiguity, complexity and non-trivial causality. FCMs are well-known due to the transparency achieve...

Rough Cognitive Ensembles (RCEs) has recently emerged as a granular multiclassifier system composed of a set of Rough Cognitive Networks (RCNs), each operating at a different granularity degree. While this model is capable of outperforming several traditional classifiers reported in the literature, there is still room for enhancing its performance....

Fuzzy Cognitive Maps (FCMs) can be defined as recurrent neural networks that allow modeling complex systems using concepts and causal relations. While this Soft Computing model have proven to be a valuable knowledge-based tool for building Decision Support Systems, further improvements are still required. In this paper, we focus on designing a FCM-...

Recently, a learning procedure to improve the convergence of sigmoid Fuzzy Cognitive Maps was proposed. This algorithm estimates the slope of each sigmoid neuron while preserving the causal weights. This paper proposes a more realistic error function for this algorithm, which is based on i) the dissimilarity between two consecutive responses, and i...

As a result of the rapid increase of online shopping, and the movement of products in and out among various countries, governments made it mandatory to pay a certain fee referred to as customs to maintain each country's economy, based on some rules i.e. weight and price. This paper proposes a model to anticipate and inform the customer if his order...

Rough Cognitive Networks (RCNs) are a kind of granular neural network that augment the reasoning rule present in Fuzzy Cognitive Maps with crisp information granules coming from Rough Set Theory. While RCNs have shown promise in solving different classification problems, this model is still very sensitive to the similarity threshold upon which the...

This research study proposes a new method for automatic design of Fuzzy Cognitive Maps (FCM) using ordinal data based on the efficient capabilities of mixed graphical models. The approach is able to model all variables on the proper domain of ordinal data by combining a new class of Mixed Graphical Models (MGMs) with a structure estimation approach...

The efficiency of e-mail campaigns is a big challenge for any e-commerce venture in terms of the response rate of e-mail campaigns and customer segmentation based on loyalty. Data mining techniques are useful tools to extract customer information related to response rate from e-mail campaigns data. This study aims at predicting customer loyalty and...

Real-life environments are inadequate to be modelled by crisp values, since human reasoning is often uncertain and ambiguous. Therefore, the aggregation of fuzzy concept of decision makers is represented sufficiently with fuzzy (imprecise) data. The purpose of this paper is the development of a powerful and useful method based on fuzzy TOPSIS which...

Rough Cognitive Networks are granular classifiers stemming from the hy-bridization of Fuzzy Cognitive Maps and Rough Set Theory. Such cognitive neural networks attempt to quantify the impact of rough granular constructs (i.e., the positive, negative and boundary regions of a target concept) over each decision class for the problem at hand. In rough...

Purpose The purpose of this paper is to provide a customer lifetime value (CLV) model to carefully assess and classify banking customers using individual measures and covering customers’ relationships with a portfolio of products of the company. Design/methodology/approach The proposed model comprises two sub-models: (sub-model 1) modelling and...

Resources can organise their work in batches, i.e. perform activities on multiple cases simultaneously, concurrently or intentionally defer activity execution to handle multiple cases (quasi-) sequentially. As batching behaviour influences process performance, efforts to gain insight on this matter are valuable. In this respect, this paper uses eve...

Extracting knowledge out of unstructured text has attracted many experts in both academia and business sectors like media, logistics, telecommunication and production. In this context, classification techniques are increasing the potential of Natural Language Processing in order to produce an efficient application of text classification in business...

Fuzzy Cognitive Maps (FCMs) have proven to be a suitable methodology for the design of knowledge-based systems. By combining both uncertainty depiction and cognitive mapping, this technique represents the knowledge of systems that are characterized by ambiguity and complexity. In short, FCMs can be defined as recurrent neural networks that include...

Process mining mainly focuses on the retrieval of process models from event logs. As these discovery algorithms make assumptions, performance analyses based on these models can present a biased view. In literature, algorithm-agnostic process metrics have been introduced. Given the critical importance of resources in the light of continuous process...

The World Wide Web supports a wide range of criminal activities such as spam-advertised e-commerce, financial fraud and malware dissemination. Although the precise motivations behind these schemes may differ, the common denominator lies in the fact that unsuspecting users visit their sites. These visits can be driven by email, web search results or...