What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
His primary areas of study are Data mining, Artificial intelligence, Machine learning, Support vector machine and Artificial neural network.
His study in the field of Knowledge extraction also crosses realms of Decision table.
His Artificial intelligence study frequently draws parallels with other fields, such as Pattern recognition.
His Machine learning research is multidisciplinary, relying on both Process modeling and Business process discovery.
His research in Support vector machine intersects with topics in Linear discriminant analysis, Rule induction, Regression analysis, Software and Key.
The study incorporates disciplines such as Feature, Computer network and Credit risk in addition to Artificial neural network.
His most cited work include:
- Benchmarking Classification Models for Software Defect Prediction: A Proposed Framework and Novel Findings (792 citations)
- Benchmarking Least Squares Support Vector Machine Classifiers (618 citations)
- Benchmarking state-of-the-art classification algorithms for credit scoring (568 citations)
What are the main themes of his work throughout his whole career to date?
Data mining, Artificial intelligence, Machine learning, Data science and Artificial neural network are his primary areas of study.
When carried out as part of a general Data mining research project, his work on Knowledge extraction is frequently linked to work in Decision table, therefore connecting diverse disciplines of study.
As part of his studies on Artificial intelligence, Bart Baesens often connects relevant areas like Pattern recognition.
His Machine learning study frequently draws connections to other fields, such as Classifier.
His Data science research includes themes of Field and Social network.
His work in Process mining addresses subjects such as Process modeling, which are connected to disciplines such as Business process discovery.
He most often published in these fields:
- Data mining (36.22%)
- Artificial intelligence (31.69%)
- Machine learning (28.54%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (31.69%)
- Analytics (13.58%)
- Machine learning (28.54%)
In recent papers he was focusing on the following fields of study:
Bart Baesens mostly deals with Artificial intelligence, Analytics, Machine learning, Data science and Data mining.
His work carried out in the field of Artificial intelligence brings together such families of science as Ranking, Scheme and Principal.
His studies in Analytics integrate themes in fields like Predictive analytics, Big data and Social network.
His study looks at the relationship between Machine learning and topics such as Profit maximization, which overlap with Evolutionary algorithm.
His research integrates issues of Supervised learning, Web scraping, Task and Service in his study of Data science.
His study on Data mining also encompasses disciplines like
- Domain, which have a strong connection to Cluster analysis and Process mining,
- Sampling that connect with fields like Benchmarking.
Between 2017 and 2021, his most popular works were:
- The value of big data for credit scoring: Enhancing financial inclusion using mobile phone data and social network analytics (37 citations)
- The value of big data for credit scoring: Enhancing financial inclusion using mobile phone data and social network analytics (37 citations)
- A multi-objective approach for profit-driven feature selection in credit scoring (24 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
His scientific interests lie mostly in Artificial intelligence, Machine learning, Analytics, Data mining and Social network.
His Artificial intelligence research is multidisciplinary, incorporating elements of Social circle and Consumer behaviour.
His work in the fields of Machine learning, such as Feature learning, Feature selection and Genetic algorithm, intersects with other areas such as Objective approach.
His Analytics research integrates issues from Financial inclusion, Added value, Credit card and Big data.
The Data mining study combines topics in areas such as Sampling, Field, Benchmarking and Domain.
His study looks at the intersection of Social network and topics like Tax rate with Decision tree.
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