Now showing items 1-6 of 6
CURIE: a cellular automaton for concept drift detection
Data stream mining extracts information from large quantities of data flowing fast and continuously (data streams). They are usually affected by changes in the data distribution, giving rise to a phenomenon referred to as ...
AT-MFCGA: An Adaptive Transfer-guided Multifactorial Cellular Genetic Algorithm for Evolutionary Multitasking
Transfer Optimization is an incipient research area dedicated to solving multiple optimization tasks simultaneously. Among the different approaches that can address this problem effectively, Evolutionary Multitasking resorts ...
LUNAR: Cellular automata for drifting data streams
With the advent of fast data streams, real-time machine learning has become a challenging task, demanding many processing resources. In addition, they can be affected by the concept drift effect, by which learning methods ...
Exploiting a Stimuli Encoding Scheme of Spiking Neural Networks for Stream Learning
Stream data processing has gained progressive momentum with the arriving of new stream applications and big data scenarios. One of the most promising techniques in stream learn- ing is the Spiking Neural Network, and some ...
On the Creation of Diverse Ensembles for Nonstationary Environments using Bio-inspired Heuristics
Recently the relevance of adaptive models for dynamic data environments has turned into a hot topic due to the vast number of sce- narios generating nonstationary data streams. When a change (concept drift) in data ...
Multi-objective heuristics applied to robot task planning for inspection plants
Robotics are generally subject to stringent operational conditions that impose a high degree of criticality on the allocation of resources and the schedule of operations in mission planning. In this regard the so-called ...