22 citations · 32 across the 7 of their papers we have counts for
9 papers
Tailoring Machine Learning for Process Mining
Paolo Ceravolo, Sylvio Barbon Junior, Ernesto Damiani +1
Machine learning models are routinely integrated into process mining pipelines to carry out tasks like data transformation, noise reduction, anomaly detection, classification, and…
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…
Improved prediction of soil properties with Multi-target Stacked Generalisation on EDXRF spectra
Everton Jose Santana, Felipe Rodrigues dos Santos, Saulo Martiello Mastelini +2
Machine Learning (ML) algorithms have been used for assessing soil quality parameters along with non-destructive methodologies. Among spectroscopic analytical methodologies, energy…
Towards meta-learning for multi-target regression problems
Gabriel Jonas Aguiar, Everton José Santana, Saulo Martiello Mastelini +2
Several multi-target regression methods were devel-oped in the last years aiming at improving predictive performanceby exploring inter-target correlation within the problem. Howeve…
Online Local Boosting: improving performance in online decision trees
Victor G. Turrisi da Costa, Saulo Martiello Mastelini, André C. Ponce de Leon Ferreira de Carvalho +1
As more data are produced each day, and faster, data stream mining is growing in importance, making clear the need for algorithms able to fast process these data. Data stream minin…
Online Multi-target regression trees with stacked leaf models
Saulo Martiello Mastelini, Sylvio Barbon, André Carlos Ponce de Leon Ferreira de Carvalho
One of the current challenges in machine learning is how to deal with data coming at increasing rates in data streams. New predictive learning strategies are needed to cope with th…
Strict Very Fast Decision Tree: a memory conservative algorithm for data stream mining
Victor Guilherme Turrisi da Costa, André Carlos Ponce de Leon Ferreira de Carvalho, Sylvio Barbon Junior
Dealing with memory and time constraints are current challenges when learning from data streams with a massive amount of data. Many algorithms have been proposed to handle these di…