36,453 research outputs found
Classification of geometrical objects by integrating currents and functional data analysis. An application to a 3D database of Spanish child population
This paper focuses on the application of Discriminant Analysis to a set of
geometrical objects (bodies) characterized by currents. A current is a relevant
mathematical object to model geometrical data, like hypersurfaces, through
integration of vector fields along them. As a consequence of the choice of a
vector-valued Reproducing Kernel Hilbert Space (RKHS) as a test space to
integrate hypersurfaces, it is possible to consider that hypersurfaces are
embedded in this Hilbert space. This embedding enables us to consider
classification algorithms of geometrical objects. A method to apply Functional
Discriminant Analysis in the obtained vector-valued RKHS is given. This method
is based on the eigenfunction decomposition of the kernel. So, the novelty of
this paper is the reformulation of a size and shape classification problem in
Functional Data Analysis terms using the theory of currents and vector-valued
RKHS. This approach is applied to a 3D database obtained from an anthropometric
survey of the Spanish child population with a potential application to online
sales of children's wear
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Approaches to conceptual clustering
Methods for Conceptual Clustering may be explicated in two lights. Conceptual Clustering methods may be viewed as extensions to techniques of numerical taxonomy, a collection of methods developed by social and natural scientists for creating classification schemes over object sets. Alternatively, conceptual clustering may be viewed as a form of learning by observation or concept formation, as opposed to methods of learning from examples or concept identification. In this paper we survey and compare a number of conceptual clustering methods along dimensions suggested by each of these views. The point we most wish to clarify is that conceptual clustering processes can be explicated as being composed of three distinct but inter-dependent subprocesses: the process of deriving a hierarchical classification scheme; the process of aggregating objects into individual classes; and the process of assigning conceptual descriptions to object classes. Each subprocess may be characterized along a number of dimensions related to search, thus facilitating a better understanding of the conceptual clustering process as a whole
The probability of default in internal ratings based (IRB) models in Basel II: an application of the rough sets methodology
El nuevo Acuerdo de Capital de junio de 2004 (Basilea II) da cabida e incentiva la
implantación de modelos propios para la medición de los riesgos financieros en las
entidades de crédito. En el trabajo que presentamos nos centramos en los modelos internos
para la valoración del riesgo de crédito (IRB) y concretamente en la aproximación a uno de
sus componentes: la probabilidad de impago (PD).
Los métodos tradicionales usados para la modelización del riesgo de crédito, como son el
análisis discriminante y los modelos logit y probit, parten de una serie de restricciones
estadísticas. La metodología rough sets se presenta como una alternativa a los métodos
estadísticos clásicos, salvando las limitaciones de estos.
En nuestro trabajo aplicamos la metodología rought sets a una base de datos, compuesta
por 106 empresas, solicitantes de créditos, con el objeto de obtener aquellos ratios que
mejor discriminan entre empresas sanas y fallidas, así como una serie de reglas de decisión
que ayudarán a detectar las operaciones potencialmente fallidas, como primer paso en la
modelización de la probabilidad de impago. Por último, enfrentamos los resultados obtenidos
con los alcanzados con el análisis discriminante clásico, para concluir que la metodología de
los rough sets presenta mejores resultados de clasificación, en nuestro caso.The new Capital Accord of June 2004 (Basel II) opens the way for and encourages credit entities to implement
their own models for measuring financial risks. In the paper presented, we focus on the use of internal rating
based (IRB) models for the assessment of credit risk and specifically on the approach to one of their
components: probability of default (PD).
In our study we apply the rough sets methodology to a database composed of 106 companies, applicants for
credit, with the object of obtaining those ratios that discriminate best between healthy and bankrupt companies,
together with a series of decision rules that will help to detect the operations potentially in default, as a first step
in modelling the probability of default. Lastly, we compare the results obtained against those obtained using
classic discriminant análisis. We conclude that the rough sets methodology presents better risk classification
results.Junta de Andalucía P06-SEJ-0153
Automated identification of Fos expression
The concentration of Fos, a protein encoded by the immediate-early gene c-fos, provides a measure of synaptic activity that may not parallel the electrical activity of neurons. Such a measure is important for the difficult problem of identifying dynamic properties of neuronal circuitries activated by a variety of stimuli and behaviours. We employ two-stage statistical pattern recognition to identify cellular nuclei that express Fos in two-dimensional sections of rat forebrain after administration of antipsychotic drugs. In stage one, we distinguish dark-stained candidate nuclei from image background by a thresholding algorithm and record size and shape measurements of these objects. In stage two, we compare performance of linear and quadratic discriminants, nearest-neighbour and artificial neural network classifiers that employ functions of these measurements to label candidate objects as either Fos nuclei, two touching Fos nuclei or irrelevant background material. New images of neighbouring brain tissue serve as test sets to assess generalizability of the best derived classification rule, as determined by lowest cross-validation misclassification rate. Three experts, two internal and one external, compare manual and automated results for accuracy assessment. Analyses of a subset of images on two separate occasions provide quantitative measures of inter- and intra-expert consistency. We conclude that our automated procedure yields results that compare favourably with those of the experts and thus has potential to remove much of the tedium, subjectivity and irreproducibility of current Fos identification methods in digital microscopy
Classification of Several Skin Cancer Types Based on Autofluorescence Intensity of Visible Light to Near Infrared Ratio
Skin cancer is a Malignant growth on the skin caused by many factors. The most common skin cancers are Basal Cell Cancer (BCC) and Squamous Cell Cancer (SCC). This research uses a discriminant analysis to classify some tissues of skin cancer based on criterion number of independent variables. An independent variable is variation of excitation light sources (LED lamp), filters, and sensors to measure autofluorescence intensity (IAF) of visible light to near infrared (VIS/NIR) ratio of paraffin embedded tissue biopsy from BCC, SCC, and Lipoma. From the result of discriminant analysis, it is known that the discriminant function is determined by 4 (four) independent variables i.e., blue LED-red filter, blue LED-yellow filter, UV LED-blue filter, and UV LED-yellow filter. The accuracy of discriminant in classifying the analysis of three skin cancer tissues is 100%
A Quantitative Criterion for Defining Planets
A simple metric can be used to determine whether a planet or exoplanet can
clear its orbital zone during a characteristic time scale, such as the lifetime
of the host star on the main sequence. This criterion requires only estimates
of star mass, planet mass, and orbital period, making it possible to
immediately classify 99% of all known exoplanets. All 8 planets and all
classifiable exoplanets satisfy the criterion. This metric may be useful in
generalizing and simplifying the definition of a planet.Comment: Accepted for publication in the Astronomical Journal; 7 pages, 6
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