A review of artificial intelligence based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models
Building energy use prediction plays an important role in building energy management and
conservation as it can help us to evaluate building energy efficiency, conduct building …
conservation as it can help us to evaluate building energy efficiency, conduct building …
Dynamic classifier selection: Recent advances and perspectives
Abstract Multiple Classifier Systems (MCS) have been widely studied as an alternative for
increasing accuracy in pattern recognition. One of the most promising MCS approaches is …
increasing accuracy in pattern recognition. One of the most promising MCS approaches is …
A dynamic ensemble learning algorithm for neural networks
This paper presents a novel dynamic ensemble learning (DEL) algorithm for designing
ensemble of neural networks (NNs). DEL algorithm determines the size of ensemble, the …
ensemble of neural networks (NNs). DEL algorithm determines the size of ensemble, the …
Modified Alexnet architecture for classification of diabetic retinopathy images
Diabetic retinopathy (DR) is an illness occurring in the eye due to increase in blood glucose
level. Among people in the age group of 70, 50% of deaths are attributed to diabetes. Early …
level. Among people in the age group of 70, 50% of deaths are attributed to diabetes. Early …
Flood detection and susceptibility map** using sentinel-1 remote sensing data and a machine learning approach: Hybrid intelligence of bagging ensemble based …
Map** flood-prone areas is a key activity in flood disaster management. In this paper, we
propose a new flood susceptibility map** technique. We employ new ensemble models …
propose a new flood susceptibility map** technique. We employ new ensemble models …
Diversity in machine learning
Machine learning methods have achieved good performance and been widely applied in
various real-world applications. They can learn the model adaptively and be better fit for …
various real-world applications. They can learn the model adaptively and be better fit for …
[CARTE][B] Ensemble methods: foundations and algorithms
ZH Zhou - 2025 - books.google.com
Ensemble methods that train multiple learners and then combine them to use, with Boosting
and Bagging as representatives, are well-known machine learning approaches. It has …
and Bagging as representatives, are well-known machine learning approaches. It has …
A survey of multiple classifier systems as hybrid systems
A current focus of intense research in pattern classification is the combination of several
classifier systems, which can be built following either the same or different models and/or …
classifier systems, which can be built following either the same or different models and/or …
State of charge estimation for Li-ion batteries using neural network modeling and unscented Kalman filter-based error cancellation
Lithium-ion batteries have been widely used as the energy storage systems in personal
portable electronics (eg cell phones, laptop computers), telecommunication systems, electric …
portable electronics (eg cell phones, laptop computers), telecommunication systems, electric …
A study on a probabilistic method for designing artificial neural networks for the formation of intelligent technology assemblies with high variability
VV Bukhtoyarov, VS Tynchenko, VA Nelyub, IS Masich… - Electronics, 2023 - mdpi.com
Currently, ensemble approaches based, among other things, on the use of non-network
models are powerful tools for solving data analysis problems in various practical …
models are powerful tools for solving data analysis problems in various practical …