Browsing by Author "Bifet, Albert"
Now showing items 1-5 of 46
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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 ... -
Adaptive XGBoost for evolving data streams
(IEEE, 2020)Boosting is an ensemble method that combines base models in a sequential manner to achieve high predictive accuracy. A popular learning algorithm based on this ensemble method is eXtreme Gradient Boosting (XGB). We present ... -
Analyzing and repairing concept drift adaptation in data stream classification
(2021)Data collected over time often exhibit changes in distribution, or concept drift, caused by changes in factors relevant to the classification task, e.g. weather conditions. Incorporating all relevant factors into the model ... -
Batch-incremental versus instance-incremental learning in dynamic and evolving data
(Springer, 2012)Many real world problems involve the challenging context of data streams, where classifiers must be incremental: able to learn from a theoretically- infinite stream of examples using limited time and memory, while being ... -
Binding data mining and expert knowledge for one-day-ahead prediction of hourly global solar radiation
(2021)A new methodology to predict one-day-ahead hourly solar global radiation is proposed in this paper. This information is very useful to address many real problems; for instance, energy-market decision making is one of the ...
Co-authors for Albert Bifet
Supervised by Albert Bifet
Showing up to 5 theses - most recently added to Research Commons first.
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Regression modelling of spectroscopic data using lazy learning and deep neural networks
(The University of Waikato, 2022)Neural networks show promise in modelling infrared (IR) spectroscopic data, but many of the proposed solutions in the literature are either Multilayer Perceptrons with a single hidden layer or are adapted from successful ...
