Extreme Situations Prediction by MultidimenSional Heterogeneous Time Series Using Logical Decision Functions

* The work is supported by RFBR, grant 04-01-00858-a

Detection of Logical-and-Probabilistic Correlation in Time Series

An application of the heterogeneous variables system prediction method to solving the time series analysis problem with respect to the sample size is considered in this work. It is created a logical-and-probabilistic correlation from the logical decision function class. Two ways is considered. When the information about event is kept safe in the process, and when it is kept safe in depending process.

Application of the Heterogeneous System Prediction Method to Pattern Recognition Problem

* This work was financially supported by RFBR-04-01-00858.

Application of the Multivariate Prediction Method to Time Series

* This work was financially supported by RFBR-04-01-00858.

Stability analysis of tree structured decision functions

In multicriteria decision problems many values must be assigned, such as the importance of the different criteria and the values of the alternatives with respect to subjective criteria. Since these assignments are approximate, it is very important to analyze the sensitivity of results when small modifications of the assignments are made. When solving a multicriteria decision problem, it is desirable to choose a decision function that leads to a solution as stable as possible. We propose here a method based on genetic programming that produces better decision functions than the commonly used ones. The theoretical expectations are validated by case studies. © 2003 Elsevier B.V. All rights reserved.

On the Quality of Decision Functions in Pattern Recognition

The problem of decision functions quality in pattern recognition is considered. An overview of the approaches to the solution of this problem is given. Within the Bayesian framework, we suggest an approach based on the Bayesian interval estimates of quality on a finite set of events.

Kernel-based mapping of orographic rainfall enhancement in the Swiss Alps as detected by weatherradar

In this paper, we develop a data-driven methodology to characterize the likelihood of orographic precipitation enhancement using sequences of weather radar images and a digital elevation model (DEM). Geographical locations with topographic characteristics favorable to enforce repeatable and persistent orographic precipitation such as stationary cells, upslope rainfall enhancement, and repeated convective initiation are detected by analyzing the spatial distribution of a set of precipitation cells extracted from radar imagery. Topographic features such as terrain convexity and gradients computed from the DEM at multiple spatial scales as well as velocity fields estimated from sequences of weather radar images are used as explanatory factors to describe the occurrence of localized precipitation enhancement. The latter is represented as a binary process by defining a threshold on the number of cell occurrences at particular locations. Both two-class and one-class support vector machine classifiers are tested to separate the presumed orographic cells from the nonorographic ones in the space of contributing topographic and flow features. Site-based validation is carried out to estimate realistic generalization skills of the obtained spatial prediction models. Due to the high class separability, the decision function of the classifiers can be interpreted as a likelihood or susceptibility of orographic precipitation enhancement. The developed approach can serve as a basis for refining radar-based quantitative precipitation estimates and short-term forecasts or for generating stochastic precipitation ensembles conditioned on the local topography.

Analysis and spatial modeling of the indoor radon Swiss data

The present research deals with an important public health threat, which is the pollution created by radon gas accumulation inside dwellings. The spatial modeling of indoor radon in Switzerland is particularly complex and challenging because of many influencing factors that should be taken into account. Indoor radon data analysis must be addressed from both a statistical and a spatial point of view. As a multivariate process, it was important at first to define the influence of each factor. In particular, it was important to define the influence of geology as being closely associated to indoor radon. This association was indeed observed for the Swiss data but not probed to be the sole determinant for the spatial modeling. The statistical analysis of data, both at univariate and multivariate level, was followed by an exploratory spatial analysis. Many tools proposed in the literature were tested and adapted, including fractality, declustering and moving windows methods. The use of Quan-tité Morisita Index (QMI) as a procedure to evaluate data clustering in function of the radon level was proposed. The existing methods of declustering were revised and applied in an attempt to approach the global histogram parameters. The exploratory phase comes along with the definition of multiple scales of interest for indoor radon mapping in Switzerland. The analysis was done with a top-to-down resolution approach, from regional to local lev¬els in order to find the appropriate scales for modeling. In this sense, data partition was optimized in order to cope with stationary conditions of geostatistical models. Common methods of spatial modeling such as Κ Nearest Neighbors (KNN), variography and General Regression Neural Networks (GRNN) were proposed as exploratory tools. In the following section, different spatial interpolation methods were applied for a par-ticular dataset. A bottom to top method complexity approach was adopted and the results were analyzed together in order to find common definitions of continuity and neighborhood parameters. Additionally, a data filter based on cross-validation was tested with the purpose of reducing noise at local scale (the CVMF). At the end of the chapter, a series of test for data consistency and methods robustness were performed. This lead to conclude about the importance of data splitting and the limitation of generalization methods for reproducing statistical distributions. The last section was dedicated to modeling methods with probabilistic interpretations. Data transformation and simulations thus allowed the use of multigaussian models and helped take the indoor radon pollution data uncertainty into consideration. The catego-rization transform was presented as a solution for extreme values modeling through clas-sification. Simulation scenarios were proposed, including an alternative proposal for the reproduction of the global histogram based on the sampling domain. The sequential Gaussian simulation (SGS) was presented as the method giving the most complete information, while classification performed in a more robust way. An error measure was defined in relation to the decision function for data classification hardening. Within the classification methods, probabilistic neural networks (PNN) show to be better adapted for modeling of high threshold categorization and for automation. Support vector machines (SVM) on the contrary performed well under balanced category conditions. In general, it was concluded that a particular prediction or estimation method is not better under all conditions of scale and neighborhood definitions. Simulations should be the basis, while other methods can provide complementary information to accomplish an efficient indoor radon decision making.

Feature Selection for Face Detection

We present a new method to select features for a face detection system using Support Vector Machines (SVMs). In the first step we reduce the dimensionality of the input space by projecting the data into a subset of eigenvectors. The dimension of the subset is determined by a classification criterion based on minimizing a bound on the expected error probability of an SVM. In the second step we select features from the SVM feature space by removing those that have low contributions to the decision function of the SVM.

A tomada de decisão dos sujeitos em projetos organizacionais e em seu cotidiano pessoal: um estudo no contexto de diferentes nacionalidades

Este trabalho tem por objetivo estudar a tomada de decisão dos indivíduos de diferentes nacionalidades, que atuam na gestão de projetos organizacionais, em sua vida fora do âmbito profissional. Dado que as metodologias existentes na área de gestão de projetos atentam para a necessidade de um processo decisório racional, lógico e objetivo, este estudo pretende explorar até que ponto os sujeitos organizacionais extrapolam este mesmo processo decisório linear, advindo do mundo profissional, para o seu cotidiano. Os estudos acadêmicos ao longo dos anos trataram de discutir esta temática da decisão racional, linear e lógica, os quais foram capazes de refutar esta hipótese com novas perspectivas para o julgamento cognitivo dos humanos. Portanto, além deste trabalho apresentar o campo de estudo da gerência de projetos e seus conceitos, ele também aborda as diversas evoluções teóricas acerca da tomada de decisão ao longo do tempo. A partir da consideração do caráter subjetivo nas teorias de decisão apresentadas, e a limitação cognitiva que muitas vezes se impõe, este estudo busca então explorar as diferentes heurísticas (estratégias simplificadoras, atalhos mentais) de julgamento e seus respectivos vieses cognitivos. As três principais meta-heurísticas, expostas por Tversky e Kahneman em seu trabalho acadêmico de 1974 e também foco deste estudo são, respectivamente: da representatividade, da disponibilidade e da ancoragem e ajustamento. Neste trabalho é realizada uma pesquisa quantitativa com sujeitos organizacionais que trabalham com gestão de projetos, ou que tiveram alguma experiência em algum projeto nas empresas em que trabalham. Ressalta-se que este estudo não se limita ao Brasil, extendendo-se também a outros países com o mesmo público-alvo de pesquisa. Os resultados da pesquisa revelaram que os profissionais que atuam em gestão de projetos estão sujeitos a vieses cognitivos fora do âmbito organizacional, sendo que os brasileiros são os menos propensos a estes vieses, em comparação com as demais nacionalidades estudadas. Também revelou-se que o tempo de experiência profissional não contribui de modo significante para uma tomada de decisão mais racional e lógica no cotidiano pessoal.

Análise da suscetibilidade a movimentos de massa no município de Peruíbe - SP, com o apoio de um sistema integrador de informações georeferenciadas

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Impactos de los 'pools de siembra' en la estructura social agraria : Una aproximación a las transformaciones en los espacios centrales de la provincia de Santa Fe (Argentina)

En los últimos años se han producido importantes transformaciones en la activiad agrícola argentina. Una de ellas es la aparición de nuevos formas de organización de la agricultura que se denominan genéricamente 'pools de siembra', los cuales impactan en los territorios locales modificando tanto los roles y las funciones de los actores agrarios tradicionales como las relaciones que mantienen entre ellos. Bajo la hipótesis de que estas nuevas formas de organización de la agricultura, llevadas a cabo por actores de tipo más financiero que socioproductivo, aceleran los procesos de concentración y desterritorialización de la riqueza, se avanza en este trabajo sobre cuatro aspectos. En primer lugar, se caracteriza a los actores actuales vinculados a la producción primaria, considerando su trayectoria y su relación con la actividad agrícola y con el espacio local. En segundo lugar, nos concentramos en esas nuevas formas de organización de la agricultura, tratando de mostrar que detrás de la denominación de 'pools de siembra' se esconde una diversidad de actores; para ello, el presente estudio articula tres variables principales: la duración del emprendimiento agrícola, la forma jurídica adoptada y el actor responsable de la gestión. En tercer lugar, se profundiza en las lógicas decisorias de los propietarios rentistas, actores clave junto con los contratistas en el avance de los pools de siembra. Por último, se describe algunos de los impactos actualmente perceptibles de estas nuevas formas de organización en los espacios centrales santafesinos

Impactos de los 'pools de siembra' en la estructura social agraria : Una aproximación a las transformaciones en los espacios centrales de la provincia de Santa Fe (Argentina)

En los últimos años se han producido importantes transformaciones en la activiad agrícola argentina. Una de ellas es la aparición de nuevos formas de organización de la agricultura que se denominan genéricamente 'pools de siembra', los cuales impactan en los territorios locales modificando tanto los roles y las funciones de los actores agrarios tradicionales como las relaciones que mantienen entre ellos. Bajo la hipótesis de que estas nuevas formas de organización de la agricultura, llevadas a cabo por actores de tipo más financiero que socioproductivo, aceleran los procesos de concentración y desterritorialización de la riqueza, se avanza en este trabajo sobre cuatro aspectos. En primer lugar, se caracteriza a los actores actuales vinculados a la producción primaria, considerando su trayectoria y su relación con la actividad agrícola y con el espacio local. En segundo lugar, nos concentramos en esas nuevas formas de organización de la agricultura, tratando de mostrar que detrás de la denominación de 'pools de siembra' se esconde una diversidad de actores; para ello, el presente estudio articula tres variables principales: la duración del emprendimiento agrícola, la forma jurídica adoptada y el actor responsable de la gestión. En tercer lugar, se profundiza en las lógicas decisorias de los propietarios rentistas, actores clave junto con los contratistas en el avance de los pools de siembra. Por último, se describe algunos de los impactos actualmente perceptibles de estas nuevas formas de organización en los espacios centrales santafesinos

Impactos de los 'pools de siembra' en la estructura social agraria : Una aproximación a las transformaciones en los espacios centrales de la provincia de Santa Fe (Argentina)

En los últimos años se han producido importantes transformaciones en la activiad agrícola argentina. Una de ellas es la aparición de nuevos formas de organización de la agricultura que se denominan genéricamente 'pools de siembra', los cuales impactan en los territorios locales modificando tanto los roles y las funciones de los actores agrarios tradicionales como las relaciones que mantienen entre ellos. Bajo la hipótesis de que estas nuevas formas de organización de la agricultura, llevadas a cabo por actores de tipo más financiero que socioproductivo, aceleran los procesos de concentración y desterritorialización de la riqueza, se avanza en este trabajo sobre cuatro aspectos. En primer lugar, se caracteriza a los actores actuales vinculados a la producción primaria, considerando su trayectoria y su relación con la actividad agrícola y con el espacio local. En segundo lugar, nos concentramos en esas nuevas formas de organización de la agricultura, tratando de mostrar que detrás de la denominación de 'pools de siembra' se esconde una diversidad de actores; para ello, el presente estudio articula tres variables principales: la duración del emprendimiento agrícola, la forma jurídica adoptada y el actor responsable de la gestión. En tercer lugar, se profundiza en las lógicas decisorias de los propietarios rentistas, actores clave junto con los contratistas en el avance de los pools de siembra. Por último, se describe algunos de los impactos actualmente perceptibles de estas nuevas formas de organización en los espacios centrales santafesinos

Expressive power of binary relevance and chain classifiers based on Bayesian Networks for multi-label classification

Bayesian network classifiers are widely used in machine learning because they intuitively represent causal relations. Multi-label classification problems require each instance to be assigned a subset of a defined set of h labels. This problem is equivalent to finding a multi-valued decision function that predicts a vector of h binary classes. In this paper we obtain the decision boundaries of two widely used Bayesian network approaches for building multi-label classifiers: Multi-label Bayesian network classifiers built using the binary relevance method and Bayesian network chain classifiers. We extend our previous single-label results to multi-label chain classifiers, and we prove that, as expected, chain classifiers provide a more expressive model than the binary relevance method.