5 citations · 9 across the 7 of their papers we have counts for
5 papers · 1 filter
Problem-oriented AutoML in Clustering
Matheus Camilo da Silva, Gabriel Marques Tavares, Eric Medvet +1
The Problem-oriented AutoML in Clustering (PoAC) framework introduces a novel, flexible approach to automating clustering tasks by addressing the shortcomings of traditional AutoML…
Decision Predicate Graphs: Enhancing Interpretability in Tree Ensembles
Leonardo Arrighi, Luca Pennella, Gabriel Marques Tavares +1
Understanding the decisions of tree-based ensembles and their relationships is pivotal for machine learning model interpretation. Recent attempts to mitigate the human-in-the-loop…
Trace Encoding in Process Mining: a survey and benchmarking
Sylvio Barbon, Paolo Ceravolo, Rafael S. Oyamada +1
Encoding methods are employed across several process mining tasks, including predictive process monitoring, anomalous case detection, trace clustering, etc. These methods are usual…
Selecting Optimal Trace Clustering Pipelines with AutoML
Sylvio Barbon, Paolo Ceravolo, Ernesto Damiani +1
Trace clustering has been extensively used to preprocess event logs. By grouping similar behavior, these techniques guide the identification of sub-logs, producing more understanda…
Using Meta-learning to Recommend Process Discovery Methods
Sylvio Barbon, Paolo Ceravolo, Ernesto Damiani +1
Process discovery methods have obtained remarkable achievements in Process Mining, delivering comprehensible process models to enhance management capabilities. However, selecting t…