Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Global Optimization of Econometric Functions
Journal of Global Optimization
Calibrating Probability Density Forecasts with Multi-objective Search
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Pareto evolutionary neural networks
IEEE Transactions on Neural Networks
Conditional Density Estimation with Class Probability Estimators
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
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In this paper, we show that the optimisation of density forecasting models for regression in machine learning can be formulated as a multi-objective problem. We describe the two objectives of sharpness and calibration and suggest suitable scoring metrics for both. We use the popular negative log-likelihood as a measure of sharpness and the probability integral transform as a measure of calibration.To optimise density forecasting models under multiple criteria we introduce a multi-objective evolutionary optimisation framework that can produce better density forecasts from a prediction user's perspective. Our experiments show improvements over the state-of-the-art on a risk management problem.