Textural classification of land cover using support vector machines: an empirical comparison with parametric, non parametric and hybrid classifiers in the Bolivian Amazon

Land cover classification is a key research field in remote sensing and land change science as thematic maps derived from remotely sensed data have become the basis for analyzing many socio-ecological issues. However, land cover classification remains a difficult task and it is especially challenging in heterogeneous tropical landscapes where nonetheless such maps are of great importance. The present study aims to establish an efficient classification approach to accurately map all broad land cover classes in a large, heterogeneous tropical area of Bolivia, as a basis for further studies (e.g., land cover-land use change). Specifically, we compare the performance of parametric (maximum likelihood), non-parametric (k-nearest neighbour and four different support vector machines - SVM), and hybrid classifiers, using both hard and soft (fuzzy) accuracy assessments. In addition, we test whether the inclusion of a textural index (homogeneity) in the classifications improves their performance. We classified Landsat imagery for two dates corresponding to dry and wet seasons and found that non-parametric, and particularly SVM classifiers, outperformed both parametric and hybrid classifiers. We also found that the use of the homogeneity index along with reflectance bands significantly increased the overall accuracy of all the classifications, but particularly of SVM algorithms. We observed that improvements in producer’s and user’s accuracies through the inclusion of the homogeneity index were different depending on land cover classes. Earlygrowth/degraded forests, pastures, grasslands and savanna were the classes most improved, especially with the SVM radial basis function and SVM sigmoid classifiers, though with both classifiers all land cover classes were mapped with producer’s and user’s accuracies of around 90%. Our approach seems very well suited to accurately map land cover in tropical regions, thus having the potential to contribute to conservation initiatives, climate change mitigation schemes such as REDD+, and rural development policies.

International consensus conference on PFAPA syndrome: evaluation of a new set of classification criteria

INTRODUCTION: PFAPA syndrome is characterized by periodic fever, associated with pharyngitis, cervical adenitis and/or aphthous stomatitis and belongs to the auto-inflammatory diseases. Diagnostic criteria are based on clinical features and the exclusion of other periodic fever syndromes. An analysis of a large cohort of patients has shown weaknesses for these criteria and there is a lack of international consensus. An International Conference was held in Morges in November 2008 to propose a new set of classification criteria based on a consensus among experts in the field.OBJECTIVE: We aimed to verify the applicability of the new set of classification criteria.PATIENTS & METHODS: 80 patients diagnosed with PFAPA syndrome from 3 centers (Genoa, Lausanne and Geneva) for pediatric rheumatology were included in the study. A detailed description of the clinical and laboratory features was obtained. The new classification criteria and the actual diagnostic criteria were applied to the patients.RESULTS: Only 40/80 patients (50%) fulfilled all criteria of the new classification. 31 patients were excluded because they didn't meet one of the 7 diagnostic criteria, 7 because of 2 criteria, and one because of 3 criteria. When we applied the current criteria to the same patients, 11/80 patients (13.7%) needed to be excluded. 8/80 patients (10%) were excluded from both sets. Exclusion was related only to some of the criteria. Number of patients for each not fulfilled criterion (new set of criteria/actual criteria): age (1/6), symptoms between episodes (2/2), delayed growth (4/1), main symptoms (21/0), periodicity, length of fever, interval between episodes, and length of disease (20/0). The application of some of the new criteria was not easy, as they were both very restrictive and needed precise information from the patients.CONCLUSION: Our work has shown that the new set of classification criteria can be applied to patients suspected for PFAPA syndrome, but it seems to be more restrictive than the actual diagnostic criteria. A further work of validation needs to be done in order to determine if this new set of classification criteria allow a good discrimination between PFAPA patients and other causes of recurrent fever syndromes.

Text2Emotion: un gestor d'experiències SOA : extracció d'emocions per al suport a la gestió de relació amb clients

Customer Experience Management (CEM) se ha convertido en un factor clave para el éxito de las empresas. CEM gestiona todas las experiencias que un cliente tiene con un proveedor de servicios o productos. Es muy importante saber como se siente un cliente en cada contacto y entonces poder sugerir automáticamente la próxima tarea a realizar, simplificando tareas realizadas por personas. En este proyecto se desarrolla una solución para evaluar experiencias. Primero se crean servicios web que clasifican experiencias en estados emocionales dependiendo del nivel de satisfacción, interés, … Esto es realizado a través de minería de textos. Se procesa y clasifica información no estructurada (documentos de texto) que representan o describen las experiencias. Se utilizan métodos de aprendizaje supervisado. Esta parte es desarrollada con una arquitectura orientada a servicios (SOA) para asegurar el uso de estándares y que los servicios sean accesibles por cualquier aplicación. Estos servicios son desplegados en un servidor de aplicaciones. En la segunda parte se desarrolla dos aplicaciones basadas en casos reales. En esta fase Cloud computing es clave. Se utiliza una plataforma de desarrollo en línea para crear toda la aplicación incluyendo tablas, objetos, lógica de negocio e interfaces de usuario. Finalmente los servicios de clasificación son integrados a la plataforma asegurando que las experiencias son evaluadas y que las tareas de seguimiento son automáticamente creadas.

Machine learning algorithms for spatial data. Case studies: Environmental pollution, natural hazards, renewable resources

This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.

Meta-analysis based SVM classification enables accurate detection of Alzheimer's disease across different clinical centers using FDG-PET and MRI.

The application of support vector machine classification (SVM) to combined information from magnetic resonance imaging (MRI) and [F18]fluorodeoxyglucose positron emission tomography (FDG-PET) has been shown to improve detection and differentiation of Alzheimer's disease dementia (AD) and frontotemporal lobar degeneration. To validate this approach for the most frequent dementia syndrome AD, and to test its applicability to multicenter data, we randomly extracted FDG-PET and MRI data of 28 AD patients and 28 healthy control subjects from the database provided by the Alzheimer's Disease Neuroimaging Initiative (ADNI) and compared them to data of 21 patients with AD and 13 control subjects from our own Leipzig cohort. SVM classification using combined volume-of-interest information from FDG-PET and MRI based on comprehensive quantitative meta-analyses investigating dementia syndromes revealed a higher discrimination accuracy in comparison to single modality classification. For the ADNI dataset accuracy rates of up to 88% and for the Leipzig cohort of up to 100% were obtained. Classifiers trained on the ADNI data discriminated the Leipzig cohorts with an accuracy of 91%. In conclusion, our results suggest SVM classification based on quantitative meta-analyses of multicenter data as a valid method for individual AD diagnosis. Furthermore, combining imaging information from MRI and FDG-PET might substantially improve the accuracy of AD diagnosis.

The IC3D classification of the corneal dystrophies.

BACKGROUND: The recent availability of genetic analyses has demonstrated the shortcomings of the current phenotypic method of corneal dystrophy classification. Abnormalities in different genes can cause a single phenotype, whereas different defects in a single gene can cause different phenotypes. Some disorders termed corneal dystrophies do not appear to have a genetic basis. PURPOSE: The purpose of this study was to develop a new classification system for corneal dystrophies, integrating up-to-date information on phenotypic description, pathologic examination, and genetic analysis. METHODS: The International Committee for Classification of Corneal Dystrophies (IC3D) was created to devise a current and accurate nomenclature. RESULTS: This anatomic classification continues to organize dystrophies according to the level chiefly affected. Each dystrophy has a template summarizing genetic, clinical, and pathologic information. A category number from 1 through 4 is assigned, reflecting the level of evidence supporting the existence of a given dystrophy. The most defined dystrophies belong to category 1 (a well-defined corneal dystrophy in which a gene has been mapped and identified and specific mutations are known) and the least defined belong to category 4 (a suspected dystrophy where the clinical and genetic evidence is not yet convincing). The nomenclature may be updated over time as new information regarding the dystrophies becomes available. CONCLUSIONS: The IC3D Classification of Corneal Dystrophies is a new classification system that incorporates many aspects of the traditional definitions of corneal dystrophies with new genetic, clinical, and pathologic information. Standardized templates provide key information that includes a level of evidence for there being a corneal dystrophy. The system is user-friendly and upgradeable and can be retrieved on the website www.corneasociety.org/ic3d.

Hypertensive portal colopathy in schistosomiasis mansoni: proposal for a classification

Portal hypertension is a frequent complication of chronic liver disease, detected not only in schistosomiasis, but also in cirrhosis of any etiology. Vascular alterations in the colonic mucosa are a potential source for acute or chronic bleeding and have been observed in patients with portal hypertension. The purpose of this prospective study was to describe and propose a classification for the vascular alterations of portal hypertension in the colonic mucosa among patients with hepatosplenic schistosomiasis mansoni. One or more alterations of portal colopathy were observed in all patients and they were classified according to their intensity, obeying the classification proposed by the authors. Portal colopathy is an important finding in hepatosplenic schistosomiasis and might be the cause of lower gastrointestinal bleeding in patients with severe portal hypertension.

IEF pattern classification-derived criteria for the identification of epoetin-delta in urine.

Epoetin-delta (Dynepo Shire Pharmaceuticals, Basing stoke, UK) is a synthetic form of erythropoietin (EPO) whose resemblance with endogenous EPO makes it hard to identify using the classical identification criteria. Urine samples collected from six healthy volunteers treated with epoetin-delta injections and from a control population were immuno-purified and analyzed with the usual IEF method. On the basis of the EPO profiles integration, a linear multivariate model was computed for discriminant analysis. For each sample, a pattern classification algorithm returned a bands distribution and intensity score (bands intensity score) saying how representative this sample is of one of the two classes, positive or negative. Effort profiles were also integrated in the model. The method yielded a good sensitivity versus specificity relation and was used to determine the detection window of the molecule following multiple injections. The bands intensity score, which can be generalized to epoetin-alpha and epoetin-beta, is proposed as an alternative criterion and a supplementary evidence for the identification of EPO abuse.

Binary classification of dyslipidemia from the waist-to-hip ratio and body mass index: a comparison of linear, logistic, and CART models.

BACKGROUND: We sought to improve upon previously published statistical modeling strategies for binary classification of dyslipidemia for general population screening purposes based on the waist-to-hip circumference ratio and body mass index anthropometric measurements. METHODS: Study subjects were participants in WHO-MONICA population-based surveys conducted in two Swiss regions. Outcome variables were based on the total serum cholesterol to high density lipoprotein cholesterol ratio. The other potential predictor variables were gender, age, current cigarette smoking, and hypertension. The models investigated were: (i) linear regression; (ii) logistic classification; (iii) regression trees; (iv) classification trees (iii and iv are collectively known as "CART"). Binary classification performance of the region-specific models was externally validated by classifying the subjects from the other region. RESULTS: Waist-to-hip circumference ratio and body mass index remained modest predictors of dyslipidemia. Correct classification rates for all models were 60-80%, with marked gender differences. Gender-specific models provided only small gains in classification. The external validations provided assurance about the stability of the models. CONCLUSIONS: There were no striking differences between either the algebraic (i, ii) vs. non-algebraic (iii, iv), or the regression (i, iii) vs. classification (ii, iv) modeling approaches. Anticipated advantages of the CART vs. simple additive linear and logistic models were less than expected in this particular application with a relatively small set of predictor variables. CART models may be more useful when considering main effects and interactions between larger sets of predictor variables.

Nosology and classification of genetic skeletal disorders: 2010 revision.

Genetic disorders involving the skeletal system arise through disturbances in the complex processes of skeletal development, growth and homeostasis and remain a diagnostic challenge because of their variety. The Nosology and Classification of Genetic Skeletal Disorders provides an overview of recognized diagnostic entities and groups them by clinical and radiographic features and molecular pathogenesis. The aim is to provide the Genetics, Pediatrics and Radiology community with a list of recognized genetic skeletal disorders that can be of help in the diagnosis of individual cases, in the delineation of novel disorders, and in building bridges between clinicians and scientists interested in skeletal biology. In the 2010 revision, 456 conditions were included and placed in 40 groups defined by molecular, biochemical, and/or radiographic criteria. Of these conditions, 316 were associated with mutations in one or more of 226 different genes, ranging from common, recurrent mutations to "private" found in single families or individuals. Thus, the Nosology is a hybrid between a list of clinically defined disorders, waiting for molecular clarification, and an annotated database documenting the phenotypic spectrum produced by mutations in a given gene. The Nosology should be useful for the diagnosis of patients with genetic skeletal diseases, particularly in view of the information flood expected with the novel sequencing technologies; in the delineation of clinical entities and novel disorders, by providing an overview of established nosologic entities; and for scientists looking for the clinical correlates of genes, proteins and pathways involved in skeletal biology. © 2011 Wiley-Liss, Inc.

Indicators of inequality: Classification and selection of ethnic health disparity indicators

Classification and selection of ethnic disparity health indicators in New Zealand

Proposal of a new classification for optimising outcome assessment following partial cricotracheal resections in severe pediatric subglottic stenosis.

OBJECTIVE: Creation of a patent subglottic airway after partial cricotracheal resection (PCTR) may not always result in successful decannulation due to associated parameters such as co-morbidity and/or glottic involvement. We classified patients after incorporating these additional parameters into the original Myer-Cotton classification to assess whether this could better predict the outcome measures after PCTR. METHODS: One hundred children with Myer-Cotton grade III or IV subglottic stenosis who underwent PCTR between 1978 and 2008 were identified from a prospectively collected database. The patients were classified into four groups based on the association of co-morbidity and/or glottic involvement. Delay in decannulation, revision open surgery and rates of decannulation were the outcome measures compared between the groups. RESULTS: There were 68 children with Myer-Cotton grade III and 32 children with grade IV stenosis. Based on the new classification, there were 36 children with isolated SGS, 31 with associated co-morbidity, 19 with associated glottic involvement and 14 children with both co-morbidity and glottic involvement. A trend towards less optimal results was noticed with the association of co-morbidity and/or glottic involvement. Statistical significance was reached for maximum decannulation failure in the group with both co-morbidity and glottic involvement. Delayed decannulation significantly correlated in the group with associated glottic involvement. CONCLUSION: This new classification is relatively simple and aimed at providing more accurate and uniform prognostic information to both patients and surgeons when dealing with the whole spectrum of severe SGS.