160 citations · 161 across the 5 of their papers we have counts for
8 papers
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…
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…