160 citations · 243 across the 8 of their papers we have counts for
11 papers
Leveraging Large Language Models for Tacit Knowledge Discovery in Organizational Contexts
Gianlucca Zuin, Saulo Mastelini, Túlio Loures +1
Documenting tacit knowledge in organizations can be a challenging task due to incomplete initial information, difficulty in identifying knowledgeable individuals, the interplay of…
A Neuro-Symbolic Explainer for Rare Events: A Case Study on Predictive Maintenance
João Gama, Rita P. Ribeiro, Saulo Mastelini +2
Predictive Maintenance applications are increasingly complex, with interactions between many components. Black box models are popular approaches based on deep learning techniques d…
MegazordNet: combining statistical and machine learning standpoints for time series forecasting
Angelo Garangau Menezes, Saulo Martiello Mastelini
Forecasting financial time series is considered to be a difficult task due to the chaotic feature of the series. Statistical approaches have shown solid results in some specific pr…
Machine learning unveils composition-property relationships in chalcogenide glasses
Saulo M. Mastelini, Daniel R. Cassar, Edesio Alcobaça +3
Due to their unique optical and electronic functionalities, chalcogenide glasses are materials of choice for numerous microelectronic and photonic devices. However, to extend the r…
River: machine learning for streaming data in Python
Jacob Montiel, Max Halford, Saulo Martiello Mastelini +8
River is a machine learning library for dynamic data streams and continual learning. It provides multiple state-of-the-art learning methods, data generators/transformers, performan…
Using dynamical quantization to perform split attempts in online tree regressors
Saulo Martiello Mastelini, Andre Carlos Ponce de Leon Ferreira de Carvalho
A central aspect of online decision tree solutions is evaluating the incoming data and enabling model growth. For such, trees much deal with different kinds of input features and p…
Predicting and interpreting oxide glass properties by machine learning using large datasets
Daniel R. Cassar, Saulo Martiello Mastelini, Tiago Botari +3
With the advent of powerful computer simulation techniques, it is time to move from the widely used knowledge-guided empirical methods to approaches driven by data science, mainly…
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…