Browsing by Author "Barddal, Jean Paul"
Now showing items 1-4 of 4
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Adaptive random forests for evolving data stream classification
(Springer, 2017)Random forests is currently one of the most used machine learning algorithms in the non-streaming (batch) setting. This preference is attributable to its high learning performance and low demands with respect to input ... -
Boosting decision stumps for dynamic feature selection on data streams
(2019)Feature selection targets the identification of which features of a dataset are relevant to the learning task. It is also widely known and used to improve computation times, reduce computation requirements, and to decrease ... -
On dynamic feature weighting for feature drifting data streams
(Springer, 2016)The ubiquity of data streams has been encouraging the development of new incremental and adaptive learning algorithms. Data stream learners must be fast, memory-bounded, but mainly, tailored to adapt to possible changes ... -
A survey on feature drift adaptation: Definition, benchmark, challenges and future directions
(Elsevier, 2017)Data stream mining is a fast growing research topic due to the ubiquity of data in several real-world problems. Given their ephemeral nature, data stream sources are expected to undergo changes in data distribution, a ...
