Centre dedocumentation
scientifique

Ensemble learning for hyperspectral image classification using tangent collaborative representation / Hongjun Su in IEEE Transactions on geoscience and remote sensing, vol 58 n° 6 (June 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 6 (June 2020) . - pp 3778 - 3790 Titre : Ensemble learning for hyperspectral image classification using tangent collaborative representation Type de document : Article/Communication Auteurs : Hongjun Su, Auteur ; Yao Yu, Auteur ; Qian Du, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 3778 - 3790 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse d'image numérique [Termes descripteurs IGN] boosting adapté [Termes descripteurs IGN] Bootstrap (statistique) [Termes descripteurs IGN] classification [Termes descripteurs IGN] classification dirigée [Termes descripteurs IGN] classification orientée objet [Termes descripteurs IGN] conception collaborative [Termes descripteurs IGN] données d'apprentissage [Termes descripteurs IGN] échantillon [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] neurone artificiel [Termes descripteurs IGN] performance [Termes descripteurs IGN] régression Résumé : (auteur) Recently, collaborative representation classification (CRC) has attracted much attention for hyperspectral image analysis. In particular, tangent space CRC (TCRC) has achieved excellent performance for hyperspectral image classification in a simplified tangent space. In this article, novel Bagging-based TCRC (TCRC-bagging) and Boosting-based TCRC (TCRC-boosting) methods are proposed. The main idea of TCRC-bagging is to generate diverse TCRC classification results using the bootstrap sample method, which can enhance the accuracy and diversity of a single classifier simultaneously. For TCRC-boosting, it can provide the most informative training samples by changing their distributions dynamically for each base TCRC learner. The effectiveness of the proposed methods is validated using three real hyperspectral data sets. The experimental results show that both TCRC-bagging and TCRC-boosting outperform their single classifier counterpart. In particular, the TCRC-boosting provides superior performance compared with the TCRC-bagging. Numéro de notice : 25280 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2957135 date de publication en ligne : 01/01/2020 En ligne : https://doi.org/10.1109/TGRS.2019.2957135 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95100 [article]

Similarity measurement on human mobility data with spatially weighted structural similarity index (SpSSIM) / Chanwoo Jin in Transactions in GIS, Vol 24 n° 1 (February 2020)  

Public [article]  inTransactions in GIS > Vol 24 n° 1 (February 2020) . - pp 104 - 122 Titre : Similarity measurement on human mobility data with spatially weighted structural similarity index (SpSSIM) Type de document : Article/Communication Auteurs : Chanwoo Jin, Auteur ; Atsushi Nara, Auteur ; Jiue-An Yang, Auteur ; Ming-Hsiang Tsou, Auteur Année de publication : 2020 Article en page(s) : pp 104 - 122 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] Bootstrap (statistique) [Termes descripteurs IGN] Californie (Etats-Unis) [Termes descripteurs IGN] carte de mobilité [Termes descripteurs IGN] données issues des réseaux sociaux [Termes descripteurs IGN] interpolation par pondération de zones [Termes descripteurs IGN] mesure de similitude [Termes descripteurs IGN] mobilité [Termes descripteurs IGN] mobilité urbaine [Termes descripteurs IGN] origine - destination Résumé : (auteur) Understanding diverse characteristics of human mobility provides profound knowledge of urban dynamics and complexity. Human movements are recorded in a variety of data sources and each describes unique mobility characteristics. Revealing similarity and difference in mobility data sources facilitates grasping comprehensive human mobility patterns. This study introduces a new method to measure similarities on two origin–destination (OD) matrices by spatially extending an image‐assessment tool, the structural similarity index (SSIM). The new measurement, spatially weighted SSIM (SpSSIM), utilizes weight matrices to overcome the SSIM sensitivity issue due to the ordering of OD pairs by explicitly defining spatial adjacency. To evaluate SpSSIM, we compared performances between SSIM and SpSSIM with resampling the orders of OD pairs and conducted bootstrapping to test the statistical significance of SpSSIM. As a case study, we compared OD matrices generated from three data sources in San Diego County, CA: U.S. Census‐based Longitudinal Employer–Household Dynamics Origin–Destination employment statistics, Twitter, and Instagram. The case study demonstrated that SpSSIM was able to capture similarities of mobility patterns between datasets that varied by distance. Some regions showed local dissimilarity while the global index indicated they were similar. The results enhance the understanding of complex mobility patterns from various datasets, including social media. Numéro de notice : A2020-104 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12590 date de publication en ligne : 23/10/2019 En ligne : https://doi.org/10.1111/tgis.12590 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94698 [article]

Parametric bootstrap estimators for hybrid inference in forest inventories / Mathieu Fortin in Forestry, an international journal of forest research, vol 91 n° 3 (July 2018)  

Public [article]  inForestry, an international journal of forest research > vol 91 n° 3 (July 2018) . - pp 354 - 365 Titre : Parametric bootstrap estimators for hybrid inference in forest inventories Type de document : Article/Communication Auteurs : Mathieu Fortin, Auteur ; Ruben Manso, Auteur ; Robert Schneider, Auteur Année de publication : 2018 Article en page(s) : pp 354 - 365 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] Bootstrap (statistique) [Termes descripteurs IGN] complexité [Termes descripteurs IGN] erreur systématique [Termes descripteurs IGN] inférence statistique [Termes descripteurs IGN] inventaire forestier (techniques et méthodes) [Termes descripteurs IGN] inventaire forestier étranger (données) [Termes descripteurs IGN] modèle stochastique [Termes descripteurs IGN] Québec (Canada) [Termes descripteurs IGN] variance [Vedettes matières IGN] Inventaire forestier Résumé : (Auteur) In forestry, the variable of interest is not always directly available from forest inventories. Consequently, practitioners have to rely on models to obtain predictions of this variable of interest. This context leads to hybrid inference, which is based on both the probability design and the model. Unfortunately, the current analytical hybrid estimators for the variance of the point estimator are mainly based on linear or nonlinear models and their use is limited when the model reaches a high level of complexity. An alternative consists of using a variance estimator based on resampling methods (Rubin, D. B. (1987). Multiple imputation for nonresponse surveys. John Wiley & Sons, Hoboken, New Jersey, USA). However, it turns out that a parametric bootstrap (BS) estimator of the variance can be biased in contexts of hybrid inference. In this study, we designed and tested a corrected BS estimator for the variance of the point estimator, which can easily be implemented as long as all of the stochastic components of the model can be properly simulated. Like previous estimators, this corrected variance estimator also makes it possible to distinguish the contribution of the sampling and the model to the variance of the point estimator. The results of three simulation studies of increasing complexity showed no evidence of bias for this corrected variance estimator, which clearly outperformed the BS variance estimator used in previous studies. Since the implementation of this corrected variance estimator is not much more complicated, we recommend its use in contexts of hybrid inference based on complex models. Numéro de notice : A2018-637 Thématique : FORET/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1093/forestry/cpx048 date de publication en ligne : 22/11/2017 En ligne : https://doi.org/10.1093/forestry/cpx048 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93246 [article]

Regression Modeling Strategies / Frank E. Harrell Jr. (2015)  

Public Titre : Regression Modeling Strategies : With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis Type de document : Monographie Auteurs : Frank E. Harrell Jr., Auteur Editeur : Springer International Publishing Année de publication : 2015 Importance : 582 p. Format : 18 x 26 cm ISBN/ISSN/EAN : 978-3-319-19425-7 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques [Termes descripteurs IGN] Bootstrap (statistique) [Termes descripteurs IGN] modèle de régression [Termes descripteurs IGN] modèle de simulation [Termes descripteurs IGN] R (langage) [Termes descripteurs IGN] régression linéaire [Termes descripteurs IGN] régression logistique Résumé : (éditeur) This highly anticipated second edition features new chapters and sections, 225 new references, and comprehensive R software. In keeping with the previous edition, this book is about the art and science of data analysis and predictive modeling, which entails choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasizes problem solving strategies that address the many issues arising when developing multivariable models using real data and not standard textbook examples. It includes imputation methods for dealing with missing data effectively, methods for fitting nonlinear relationships and for making the estimation of transformations a formal part of the modeling process, methods for dealing with "too many variables to analyze and not enough observations," and powerful model validation techniques based on the bootstrap. The reader will gain a keen understanding of predictive accuracy, and the harm of categorizing continuous predictors or outcomes. This text realistically deals with model uncertainty, and its effects on inference, to achieve "safe data mining." It also presents many graphical methods for communicating complex regression models to non-statisticians. Regression Modeling Strategies presents full-scale case studies of non-trivial datasets instead of over-simplified illustrations of each method. These case studies use freely available R functions that make the multiple imputation, model building, validation, and interpretation tasks described in the book relatively easy to do. Most of the methods in this text apply to all regression models, but special emphasis is given to multiple regression using generalized least squares for longitudinal data, the binary logistic model, models for ordinal responses, parametric survival regression models, and the Cox semiparametric survival model. A new emphasis is given to the robust analysis of continuous dependent variables using ordinal regression. Note de contenu : Introduction 1- General Aspects of Fitting Regression Models 2- Missing Data 3- Multivariable Modeling Strategies 4- Describing, Resampling, Validating, and Simplifying the Model 5- R Software 6- Modeling Longitudinal Responses using Generalized Least Squares 7- Case Study in Data Reduction 8- Overview of Maximum Likelihood Estimation 9- Binary Logistic Regression 10- Case Study in Binary Logistic Regression, Model Selection and Approximation: Predicting Cause of Death 11- Logistic Model Case Study 2: Survival of Titanic Passengers 12- Ordinal Logistic Regression 13- Case Study in Ordinal Regression, Data Reduction, and Penalization 14- Regression Models for Continuous Y and Case Study in Ordinal Regression 15- Transform-Both-Sides Regression 16- Introduction to Survival Analysis 17- Parametric Survival Models 18- Case Study in Parametric Survival Modeling and Model Approximation 19- Cox Proportional Hazards Regression Model 20- Case Study in Cox Regression Numéro de notice : 25813 Thématique : INFORMATIQUE/MATHEMATIQUE Nature : Monographie En ligne : https://doi.org/10.1007/978-3-319-19425-7 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95076