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9 results on '"POGGI, JEAN-MICHEL"'

1. Random Forests for Time Series

Author

Goehry, Benjamin, Yan, Hui, Goude, Yannig, Massart, Pascal, Poggi, Jean-Michel, Université Paris-Saclay, EDF (EDF), Université de Paris (UP), Statistique mathématique et apprentissage (CELESTE), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire de Mathématiques d'Orsay (LMO), Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), and Université Paris Cité (UPCité)

Subjects
Block bootstrap, Time series, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Random forests
Abstract

Random forests are a powerful learning algorithm. However, when dealing with time series, the time-dependent structure is lost, assuming the observations are independent. We propose some variants of random forests for time series. The idea is to replace standard bootstrap with a dependent block bootstrap to subsample time series during tree construction. We present numerical experiments on electricity load forecasting. The first, at a disaggregated level and the second at a national level focusing on atypical periods. For both, we explore a heuristic for the choice of the block size. Additional experiments with generic time series data are also available., REVSTAT-Statistical Journal, Vol. 21 No. 2 (2023): REVSTAT-Statistical Journal

Published
2023
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3. Random Forest-Based Approach for Physiological Functional Variable Selection: Towards Driver's Stress Level Classification

Author

El Haouij, Neska, Poggi, Jean-Michel, Ghozi, Raja, Sevestre-Ghalila, Sylvie, Jaïdane, Mériem, CEA-LinkLab, Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Telnet Innovation Labs, Laboratoire de Mathématiques d'Orsay (LM-Orsay), Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS), Model selection in statistical learning (SELECT), Laboratoire de Mathématiques d'Orsay (LMO), Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Unité de recherche Signaux et Systèmes [Tunis] (UR-U2S-ENIT), Ecole Nationale d'Ingénieurs de Tunis (ENIT), Université de Tunis El Manar (UTM)-Université de Tunis El Manar (UTM), Université Paris Descartes - Paris 5 (UPD5), Mathématiques Appliquées Paris 5 (MAP5 - UMR 8145), Université Paris Descartes - Paris 5 (UPD5)-Institut National des Sciences Mathématiques et de leurs Interactions (INSMI)-Centre National de la Recherche Scientifique (CNRS), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire de Mathématiques d'Orsay (LMO), Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS), Unité Signaux et Systèmes, Université de Tunis El Manar, 2092, Tunisia.-Ecole Nationale d'Ingénieurs de Tunis ( ENIT ), Telnet Innovation Labs, Laboratoire de Mathématiques d'Orsay ( LM-Orsay ), Université Paris-Sud - Paris 11 ( UP11 ) -Centre National de la Recherche Scientifique ( CNRS ), Model selection in statistical learning ( SELECT ), Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -Laboratoire de Mathématiques d'Orsay ( LMO ), Université Paris-Saclay-Centre National de la Recherche Scientifique ( CNRS ) -Université Paris-Saclay-Centre National de la Recherche Scientifique ( CNRS ) -Centre National de la Recherche Scientifique ( CNRS ), Université Paris Descartes - Paris 5 ( UPD5 ), Mathématiques Appliquées à Paris 5 ( MAP5 - UMR 8145 ), Université Paris Descartes - Paris 5 ( UPD5 ) -Institut National des Sciences Mathématiques et de leurs Interactions-Centre National de la Recherche Scientifique ( CNRS ), Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), and Unité Signaux et Systèmes de l'Ecole Nationale d'Ingénieurs de Tunis

Subjects
Recursive feature elimination, [STAT.AP]Statistics [stat]/Applications [stat.AP], [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], [ STAT.AP ] Statistics [stat]/Applications [stat.AP], Grouped variable importance, [ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing, Random forests, Wavelets, Physiological signals, Functional data, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, [ STAT.ML ] Statistics [stat]/Machine Learning [stat.ML]
Abstract

This paper is devoted to a statistical physiological functional variable selection for driver's stress level classification using random forests. Indeed, this study focuses on humans physiological changes, produced when driving in different urban routes, captured using portable sensors. Specifically, the electrodermal activity measured on two different locations: hand and foot, electromyogram, heart rate and respiration of ten driving experiments in three types of routes: rest area, city, and highway driving issued from drivedb database, available online on the PhysioNet website. Several studies were achieved on driver's stress level recognition using physiological signals. Classically, researchers extract expert-based features from physiological signals and select the most relevant ones for stress level recognition. This work provides a random forest-based method for the selection of physiological functional variables in order to classify the driver's stress level. On the methodological side, the contributions of this work are to consider physiological signals as functional variables, decomposed on wavelet basis and to offer a procedure of variable selection. On the applied side, the proposed method provides a " blind " procedure of driver's stress level classification performing as the expert-based study in terms of misclassification rate. It offers moreover a ranking of physiological variables according to their importance in stress level classification. The obtained results suggest that electromyogram and heart rate signals are not very relevant when compared to the electro-dermal and the respiration signals.

Published
2017

4. Aggregation of Multi-Scale Experts for Bottom-Up Load Forecasting.

Author

Goehry, Benjamin, Goude, Yannig, Massart, Pascal, and Poggi, Jean-Michel

Abstract

The development of smart grid and new advanced metering infrastructures induces new opportunities and challenges for utilities. Exploiting smart meters information for forecasting stands as a key point for energy providers who have to deal with time varying portfolio of customers as well as grid managers who needs to improve accuracy of local forecasts to face with distributed renewable energy generation development. We propose a new machine learning approach to forecast the system load of a group of customers exploiting individual load measurements in real time and/or exogenous information like weather and survey data. Our approach consists in building experts using random forests trained on some subsets of customers then normalise their predictions and aggregate them with a convex expert aggregation algorithm to forecast the system load. We propose new aggregation methods and compare two strategies for building subsets of customers: 1) hierarchical clustering based on survey data and/or load features and 2) random clustering strategy. These approaches are evaluated on a real data set of residential Irish customers load at a half hourly resolution. We show that our approaches achieve a significant gain in short term load forecasting accuracy of around 25 percent of RMSE. [ABSTRACT FROM AUTHOR]

Published
2020
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5. Random forests and big data

Author

Genuer, Robin, Poggi, Jean-Michel, Tuleau-Malot, Christine, Villa-Vialaneix, Nathalie, Statistics In System biology and Translational Medicine (SISTM), Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)- Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut de Santé Publique, d'Epidémiologie et de Développement (ISPED), Université Bordeaux Segalen - Bordeaux 2, Laboratoire de Mathématiques d'Orsay (LM-Orsay), Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS), Laboratoire Jean Alexandre Dieudonné (LJAD), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Institut National de la Recherche Agronomique (INRA), Société Française de Statistique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Epidémiologie et Biostatistique [Bordeaux], Université Bordeaux Segalen - Bordeaux 2-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Bordeaux Segalen - Bordeaux 2-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Laboratoire Jean Alexandre Dieudonné (JAD), Université Nice Sophia Antipolis (... - 2019) (UNS), Université Côte d'Azur (UCA)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11), Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS), Vialaneix, Nathalie, Institut National de Recherche en Informatique et en Automatique (Inria), Institut National de la Santé et de la Recherche Médicale (Inserm)-Université de Bordeaux, Epidémiologie et Biostatistique [Bordeaux], Université Bordeaux Segalen - Bordeaux 2-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université Paris-Sud - Paris 11 (UP11), Université Paris Descartes - Paris 5 (UPD5), COMUE Université Côte d'Azur (2015 - 2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015 - 2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS), and Société Française de Statistique (SFdS). FRA.

Subjects
Big Data, forêts aléatoires, big data, data stream, random forest, flux de données, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], Statistiques (Mathématiques), Random forests, [MATH.MATH-ST] Mathematics [math]/Statistics [math.ST], Data streams
Abstract

Big Data is one of the major challenges of statistical science and has numerous consequences from algorithmic and theoretical viewpoints. Big Data always involves massive data but it also often includes data streams and data heterogeneity. Recently some statistical methods have been adapted to process Big Data, like linear regression models, clustering methods and bootstrapping schemes. Based on decision trees combined with aggregation and bootstrap ideas, random forests, introduced by Breiman in 2001, are a powerful nonparametric statistical method allowing to consider in a single and versatile framework regression problems as well as two-class or multi-class classification problems. This paper reviews available proposals about random forests in parallel environments as well as about online random forests. Then, we formulate various remarks and sketch some alternative directions for random forests in the Big Data context., Le Big Data est un des grands défis que doit relever la statistique et a de nombreuses conséquences sur les plans théorique et algorithmique. Le Big Data implique toujours le caractère massif des donn ées mais comprend bien souvent aussi des données en flux (en ligne) et implique le traitement de données hétérogènes. Récemment certaines méthodes statistiques ont été adapt ées pour traiter le Big Data, par exemple les modèles de r égression linéaire, les méthodes de classification et les schémas de ré echantillonnage. Basées sur des arbres de d écision et exploitant les id ées d'agrégation et de bootstrap, les forêts al éatoires introduites par Breiman en 2001, sont une méthode statistique non paramétrique puissante et versatile permettant de prendre en compte dans un cadre unique tant les problemes de régression que les problèmes de classification binaire ou multi-classes. Ce papier examine les propositions disponibles de forêts aléatoires en environnement parallèle ainsi que sur les forêts aléatoires en ligne. Ensuite, nous formulons diverses remarques avant d'esquisser quelques directions alternatives pour les forêts aléatoires dans le contexte du Big Data.

Published
2015

6. Random Forests: some methodological insights

Author

Genuer, Robin, Poggi, Jean-Michel, Tuleau, Christine, Laboratoire de Mathématiques d'Orsay (LM-Orsay), Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11), Model selection in statistical learning (SELECT), Laboratoire de Mathématiques d'Orsay (LMO), Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Laboratoire Jean Alexandre Dieudonné (JAD), Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS), INRIA, Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire de Mathématiques d'Orsay (LMO), Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS), and Université Nice Sophia Antipolis (1965 - 2019) (UNS)

Subjects
Random Forests, Variable Importance, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], Variable Selection, [STAT.TH]Statistics [stat]/Statistics Theory [stat.TH], Classification, Regression
Abstract

This paper examines from an experimental perspective random forests, the increasingly used statistical method for classification and regression problems introduced by Leo Breiman in 2001. It first aims at confirming, known but sparse, advice for using random forests and at proposing some complementary remarks for both standard problems as well as high dimensional ones for which the number of variables hugely exceeds the sample size. But the main contribution of this paper is twofold: to provide some insights about the behavior of the variable importance index based on random forests and in addition, to propose to investigate two classical issues of variable selection. The first one is to find important variables for interpretation and the second one is more restrictive and try to design a good prediction model. The strategy involves a ranking of explanatory variables using the random forests score of importance and a stepwise ascending variable introduction strategy.

Published
2008

7. PM10 forecasting using clusterwise regression

Author

Poggi, Jean-Michel and Portier, Bruno

Subjects
*STATISTICAL weather forecasting, *REGRESSION analysis, *METEOROLOGICAL stations, *NONLINEAR statistical models, *WINTER, *NUMERICAL analysis
Abstract

Abstract: In this paper, we are interested in the statistical forecasting of the daily mean PM10 concentration. Hourly concentrations of PM10 have been measured in the city of Rouen, in Haute-Normandie, France. Located at northwest of Paris, near the south side of Manche sea and heavily industrialised. We consider three monitoring stations reflecting the diversity of situations: an urban background station, a traffic station and an industrial station near the cereal harbour of Rouen. We have focused our attention on data for the months that register higher values, from December to March, on years 2004–2009. The models are obtained from the winter days of the four seasons 2004/2005 to 2007/2008 (training data) and then the forecasting performance is evaluated on the winter days of the season 2008/2009 (test data). We show that it is possible to accurately forecast the daily mean concentration by fitting a function of meteorological predictors and the average concentration measured on the previous day. The values of observed meteorological variables are used for fitting the models and are also considered for the test data. We have compared the forecasts produced by three different methods: persistence, generalized additive nonlinear models and clusterwise linear regression models. This last method gives very impressive results and the end of the paper tries to analyze the reasons of such a good behavior. [Copyright &y& Elsevier]

Published
2011
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8. Variable selection using random forests

Author

Genuer, Robin, Poggi, Jean-Michel, and Tuleau-Malot, Christine

Subjects
*STATISTICS, *REGRESSION analysis, *PREDICTION theory, *TREE graphs, *MATHEMATICAL models, *MATHEMATICAL variables
Abstract

Abstract: This paper proposes, focusing on random forests, the increasingly used statistical method for classification and regression problems introduced by Leo Breiman in 2001, to investigate two classical issues of variable selection. The first one is to find important variables for interpretation and the second one is more restrictive and try to design a good parsimonious prediction model. The main contribution is twofold: to provide some experimental insights about the behavior of the variable importance index based on random forests and to propose a strategy involving a ranking of explanatory variables using the random forests score of importance and a stepwise ascending variable introduction strategy. [Copyright &y& Elsevier]

Published
2010
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9. Random forests for global sensitivity analysis: A selective review.

Author

Antoniadis, Anestis, Lambert-Lacroix, Sophie, and Poggi, Jean-Michel

Subjects
*GLOBAL analysis (Mathematics), *SENSITIVITY analysis, *RANDOM forest algorithms, *RANDOM variables, *POLYNOMIAL chaos
Abstract

The understanding of many physical and engineering problems involves running complex computational models. Such models take as input a high number of numerical and physical explanatory variables. The information on these underlying input parameters is often limited or uncertain. It is therefore important, based on the relationships between the input variables and the output, to identify and prioritize the most influential inputs. One may use global sensitivity analysis (GSA) methods which aim at ranking input random variables according to their importance in the output uncertainty, or even quantify the global influence of a particular input on the output. Using sensitivity metrics to ignore less important parameters is a form of dimension reduction in the model's input parameter space. This suggests the use of meta-modeling as a quantitative approach for nonparametric GSA, where the original input/output relation is first approximated using various statistical regression techniques. Subsequently, the main goal of our work is to provide a comprehensive review paper in the domain of sensitivity analysis focusing on some interesting connections between random forests and GSA. The idea is to use a random forests methodology as an efficient non-parametric approach for building meta-models that allow an efficient sensitivity analysis. Apart its easy applicability to regression problems, the random forests approach presents further strong advantages by its ability to implicitly deal with correlation and high dimensional data, to handle interactions between variables and to identify informative inputs using a permutation based RF variable importance index which is easy and fast to compute. We further review an adequate set of tools for quantifying variable importance which are then exploited to reduce the model's dimension enabling otherwise infeasible sensibility analysis studies. Numerical results from several simulations and a data exploration on a real dataset are presented to illustrate the effectiveness of such an approach. • Global Sensitivity Analysis ranks inputs according to their importance on output. • Random Forests is an efficient non-parametric approach for building meta-models. • Random forests variable importance measure is used to define sensitivity measures. • Provide a comprehensive review paper in sensitivity analysis using random forests. • Focus on connections between random forests and Global Sensitivity Analysis. [ABSTRACT FROM AUTHOR]

Published
2021
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