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: CoRR (2022)
Keyphrases
- energy consumption
- sensor networks
- decision trees
- data streams
- wireless sensor networks
- total energy
- ensemble methods
- energy efficiency
- ensemble classifier
- energy saving
- ensemble learning
- imbalanced data
- energy efficient
- machine learning
- tree ensembles
- classifier ensemble
- majority voting
- energy conservation
- concept drift
- data transmission
- save energy
- routing protocol
- routing algorithm
- training set
- data sets
- random forests
- classification algorithm
- base classifiers
- power management
- ensemble selection
- base station
- electricity consumption
- data aggregation
- data center
- feature selection
- multiple classifier systems
- machine learning methods
- energy aware
- residual energy
- resource limitations
- mobile devices
- learning algorithm
- query processing
- shortest path
- sensor data
- power consumption
- sensor nodes