David Martens spends much of his time researching Artificial intelligence, Data mining, Machine learning, Support vector machine and Ant colony optimization algorithms. His Artificial intelligence study spans across into fields like Information system and Inductive bias. His work carried out in the field of Data mining brings together such families of science as Social network analysis, Social network and Domain knowledge.
His work on Inductive logic programming as part of general Machine learning study is frequently connected to Workflow, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them. David Martens interconnects Artificial neural network, Key and Rule induction in the investigation of issues within Support vector machine. His Ant colony optimization algorithms study combines topics in areas such as Evolutionary algorithm, Predictive modelling and Swarm intelligence.
David Martens focuses on Artificial intelligence, Machine learning, Data mining, Support vector machine and Data science. The concepts of his Artificial intelligence study are interwoven with issues in Benchmarking and Key. His Data mining research is multidisciplinary, incorporating elements of Information extraction and Domain knowledge.
The Support vector machine study combines topics in areas such as Artificial neural network, Econometrics and Decision tree. When carried out as part of a general Econometrics research project, his work on Ordinary least squares is frequently linked to work in Loss given default, therefore connecting diverse disciplines of study. His study in Data science is interdisciplinary in nature, drawing from both Test, Sentiment analysis, Dance and Expert system.
David Martens mainly focuses on Artificial intelligence, Machine learning, Support vector machine, Data set and Data mining. His work on Classifier and Domain knowledge as part of general Artificial intelligence research is frequently linked to Ordinal optimization, Task and Active listening, bridging the gap between disciplines. His work on Feature, Deep learning and Curse of dimensionality as part of general Machine learning study is frequently linked to Sparse matrix, therefore connecting diverse disciplines of science.
His Support vector machine research incorporates elements of Lift, Econometrics, Consignee, Risk analysis and Test set. His biological study spans a wide range of topics, including Sample size determination, Benchmarking, Payment and Big data. His study on Data mining is mostly dedicated to connecting different topics, such as Selection bias.
David Martens mostly deals with Artificial intelligence, Support vector machine, Counterfactual conditional, Algorithm and Data science. The various areas that he examines in his Artificial intelligence study include Music theory, Machine learning, Music information retrieval and Natural language processing. David Martens works in the field of Machine learning, focusing on Boosting in particular.
His studies deal with areas such as Consumer confidence index, Sensitivity analysis, Uncertainty analysis, Credit default swap and Economic policy as well as Support vector machine. His Data science research includes themes of Decision support system and Big data. In his study, which falls under the umbrella issue of Predictive analytics, Payment and Consumer behaviour is strongly linked to Data set.
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Comprehensible credit scoring models using rule extraction from support vector machines
David Martens;Bart Baesens;Bart Baesens;Tony Van Gestel;Jan Vanthienen.
European Journal of Operational Research (2007)
Classification With Ant Colony Optimization
D. Martens;M. De Backer;R. Haesen;J. Vanthienen.
IEEE Transactions on Evolutionary Computation (2007)
New insights into churn prediction in the telecommunication sector: a profit driven data mining approach
Wouter Verbeke;Karel Dejaeger;David Martens;Joon Hur.
European Journal of Operational Research (2012)
Building comprehensible customer churn prediction models with advanced rule induction techniques
Wouter Verbeke;David Martens;Christophe Mues;Bart Baesens.
Expert Systems With Applications (2011)
Editorial survey: swarm intelligence for data mining
David Martens;Bart Baesens;Tom Fawcett.
Machine Learning (2011)
Data Mining Techniques for Software Effort Estimation: A Comparative Study
K. Dejaeger;W. Verbeke;D. Martens;B. Baesens.
IEEE Transactions on Software Engineering (2012)
Robust Process Discovery with Artificial Negative Events
Stijn Goedertier;David Martens;Jan Vanthienen;Bart Baesens.
Journal of Machine Learning Research (2009)
Benchmarking regression algorithms for loss given default modeling
Gert Loterman;Iain Brown;David Martens;Christophe Mues.
International Journal of Forecasting (2012)
Explaining data-driven document classifications
David Martens;Foster Provost.
Management Information Systems Quarterly (2014)
Decompositional Rule Extraction from Support Vector Machines by Active Learning
D. Martens;B.B. Baesens;T. Van Gestel.
IEEE Transactions on Knowledge and Data Engineering (2009)
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