Search results for "regression trees"
showing 9 items of 9 documents
Treed Gaussian Process Regression for Solving Offline Data-Driven Continuous Multiobjective Optimization Problems
2023
Abstract For offline data-driven multiobjective optimization problems (MOPs), no new data is available during the optimization process. Approximation models (or surrogates) are first built using the provided offline data and an optimizer, e.g. a multiobjective evolutionary algorithm, can then be utilized to find Pareto optimal solutions to the problem with surrogates as objective functions. In contrast to online data-driven MOPs, these surrogates cannot be updated with new data and, hence, the approximation accuracy cannot be improved by considering new data during the optimization process. Gaussian process regression (GPR) models are widely used as surrogates because of their ability to pr…
Modelling landscape constraints on farmland bird species range shifts under climate change
2018
Several studies estimating the effects of global environmental change on biodiversity are focused on climate change. Yet, non-climatic factors such as changes in land cover can also be of paramount importance. This may be particularly important for habitat specialists associated with human-dominated landscapes, where land cover and climate changes may be largely decoupled. Here, we tested this idea by modelling the influence of climate, landscape composition and pattern, on the predicted future (2021–2050) distributions of 21 farmland bird species in the Iberian Peninsula, using boosted regression trees and 10-km resolution presence/absence data. We also evaluated whether habitat specialist…
Predicting and mapping human risk of exposure to
2019
Background Tick-borne diseases have become increasingly common in recent decades and present a health problem in many parts of Europe. Control and prevention of these diseases require a better understanding of vector distribution. Aim Our aim was to create a model able to predict the distribution of Ixodes ricinus nymphs in southern Scandinavia and to assess how this relates to risk of human exposure. Methods We measured the presence of I. ricinus tick nymphs at 159 stratified random lowland forest and meadow sites in Denmark, Norway and Sweden by dragging 400 m transects from August to September 2016, representing a total distance of 63.6 km. Using climate and remote sensing environmental …
Contributed discussion on article by Pratola
2016
The author should be commended for his outstanding contribution to the literature on Bayesian regression tree models. The author introduces three innovative sampling approaches which allow for efficient traversal of the model space. In this response, we add a fourth alternative.
A comprehensive assessment of energy efficiency of wastewater treatment plants: An efficiency analysis tree approach
2023
Producción Científica
Estimation of total electricity consumption curves of small areas by sampling in a finite population
2016
International audience; Many studies carried out in the French electricity company EDF are based on the analysis of the total electricity consumption curves of groups of customers. These aggregated electricity consumption curves are estimated by using samples of thousands of curves measured at a small time step and collected according to a sampling design. Small area estimation is very usual in survey sampling. It is often addressed by using implicit or explicit domain models between the interest variable and the auxiliary variables. The goal here is to estimate totals of electricity consumption curves over domains or areas. Three approaches are compared: the rst one consists in modeling th…
Intelligent solutions for real-life data-driven applications
2017
The subject of this thesis belongs to the topic of machine learning or, specifically, to the development of advanced methods for regression analysis, clustering, and anomaly detection. Industry is constantly seeking improved production practices and minimized production time and costs. In connection to this, several industrial case studies are presented in which mathematical models for predicting paper quality were proposed. The most important variables for the prediction models are selected based on information-theoretic measures and regression trees approach. The rest of the original papers are devoted to unsupervised machine learning. The main focus is developing advanced spectral cluster…
Two Relevant Forecasting Problems for Practitioners in Finance: Equity Risk Premium and Non-Performing Loans
2021
The thesis aims to substantiate whether macroeconomic factors indicators are relevant to predict both in-sample and out-of-sample assets' future performance focusing on two well-studied themes in financial economics and banking: First, the ability to predict the equity risk premium, and second, the macroeconomic determinants of non-performing loans (NPL) rates. The dissertation is divided in three chapters. Chapter 1, entitled "Forecasting the equity risk premium in the European Monetary Union", investigates the capacity of multiple economic and technical variables to predict the Euro area equity risk premium. The chapter examines the performance of several variables that could be good pred…
Robust estimation of mean electricity consumption curves by sampling for small areas in presence of missing values
2017
In this thesis, we address the problem of robust estimation of mean or total electricity consumption curves by sampling in a finite population for the entire population and for small areas. We are also interested in estimating mean curves by sampling in presence of partially missing trajectories.Indeed, many studies carried out in the French electricity company EDF, for marketing or power grid management purposes, are based on the analysis of mean or total electricity consumption curves at a fine time scale, for different groups of clients sharing some common characteristics.Because of privacy issues and financial costs, it is not possible to measure the electricity consumption curve of eac…