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Validation set sampling strategies for predictive process monitoring
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Hellinger distance decision trees for PU learning in imbalanced data sets
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GeoRF : a geospatial random forest
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Graph neural networks for house price prediction : do or don't?
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LS-ICE : a load state intercase encoding framework for improved predictive monitoring of business processes
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- Journal Article
- A1
- open access
A two-step anomaly detection based method for PU classification in imbalanced data sets
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- Conference Paper
- C1
- open access
An evolutionary geospatial regression tree
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- Journal Article
- A1
- open access
A survey of methods and input data types for house price prediction
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Global conformance checking measures using shallow representation and deep learning
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Can recurrent neural networks learn process model structure?
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DyLoPro : profiling the dynamics of event logs
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- Conference Paper
- C1
- open access
Outcome-oriented predictive process monitoring on positive and unlabelled event logs
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- Conference Paper
- P1
- open access
Can deep neural networks learn process model structure? An assessment framework and analysis
(2022) PROCESS MINING WORKSHOPS, ICPM 2021. In Lecture Notes in Business Information Processing 433. p.127-139 -
Predicting the state of a house using Google Street View : an analysis of deep binary classification models for the assessment of the quality of Flemish houses
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- Conference Paper
- C1
- open access
Supervised conformance checking using recurrent neural network classifiers