Balancing Performance and Energy Consumption of Bagging Ensembles for the Classification of Data Streams in Edge Computing.
Guilherme Weigert CassalesHeitor Murilo GomesAlbert BifetBernhard PfahringerHermes SengerPublished in: IEEE Trans. Netw. Serv. Manag. (2023)
Keyphrases
- energy consumption
- sensor networks
- data streams
- decision trees
- wireless sensor networks
- energy efficient
- energy efficiency
- imbalanced data
- ensemble methods
- energy saving
- ensemble classifier
- ensemble learning
- data transmission
- tree ensembles
- total energy
- classifier ensemble
- energy conservation
- majority voting
- power management
- machine learning
- data center
- random forests
- routing algorithm
- benchmark datasets
- base station
- sensor nodes
- training set
- energy aware
- base classifiers
- classification accuracy
- multi hop
- base learners
- multiple classifier systems
- data sets
- concept drift
- stability margin
- routing protocol
- sensor data
- data aggregation
- active learning
- decision stumps
- network lifetime
- machine learning methods
- power consumption