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Title: Calibrating Probability Density Forecasts with Multi-objective Search
Author: Carney, Michael
Cunningham, Pádraig
Keywords: Computer Science
Issue Date: 10-Feb-2006
Publisher: Trinity College Dublin, Department of Computer Science
Citation: Carney, Michael; Cunningham, Pádraig. 'Calibrating Probability Density Forecasts with Multi-objective Search'. - Dublin, Trinity College Dublin, Department of Computer Science, TCD-CS-2006-07, 2006, pp12
Series/Report no.: Computer Science Technical Report
Abstract: In this paper, we show that the optimization 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. We show how optimization on negative log-likelihood alone often results in sub-optimal models. To solve this problem we introduce a multi-objective evolutionary optimization framework that can produce better density forecasts from a prediction users perspective. Our experiments show improvements over state-of-the-art approaches.
Appears in Collections:Computer Science Technical Reports

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