7 citations · 9 across the 4 of their papers we have counts for
8 papers · 1 filter
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…
An Extensive Experimental Evaluation of Automated Machine Learning Methods for Recommending Classification Algorithms (Extended Version)
Márcio P. Basgalupp, Rodrigo C. Barros, Alex G. C. de Sá +4
This paper presents an experimental comparison among four Automated Machine Learning (AutoML) methods for recommending the best classification algorithm for a given input dataset.…
MeLIME: Meaningful Local Explanation for Machine Learning Models
Tiago Botari, Frederik Hvilshøj, Rafael Izbicki +1
Most state-of-the-art machine learning algorithms induce black-box models, preventing their application in many sensitive domains. Hence, many methodologies for explaining machine…
Local Interpretation Methods to Machine Learning Using the Domain of the Feature Space
Tiago Botari, Rafael Izbicki, Andre C. P. L. F. de Carvalho
As machine learning becomes an important part of many real world applications affecting human lives, new requirements, besides high predictive accuracy, become important. One impor…
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…