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Found 5 papers, 2 papers with code
Uncertainty-Aware Vehicle Energy Efficiency Prediction using an Ensemble of Neural Networks
We propose in this paper an ensemble learning approach based on deep neural networks (ENN) that is designed to reduce the predictive uncertainty and to output measures of such uncertainty.
Uncertainty-Aware Prediction of Battery Energy Consumption for Hybrid Electric Vehicles
We propose a data-driven approach which relies on real-world datasets including battery related attributes.
Boosting Algorithms for Delivery Time Prediction in Transportation Logistics
A variety of solutions exist for short-term travel time predictions such as solutions that utilize real-time GPS data and optimization methods to track the path of a vehicle.
Diversity-Aware Weighted Majority Vote Classifier for Imbalanced Data
In this paper, we propose a diversity-aware ensemble learning based algorithm, referred to as DAMVI, to deal with imbalanced binary classification tasks.
MetaBags: Bagged Meta-Decision Trees for Regression
The proposed method and meta-features are designed in such a way that they enable good predictive performance even in subregions of space which are not adequately represented in the available training data.
