28 research outputs found
Neuronanatomy, neurology and Bayesian networks
Bayesian networks are data mining models with clear semantics and a sound theoretical foundation. In this keynote talk we will pinpoint a number of neuroscience problems that can be addressed using Bayesian networks. In neuroanatomy, we will show computer simulation models of dendritic trees and classification of neuron types, both based on morphological features. In neurology, we will present the search for genetic biomarkers in Alzheimer's disease and the prediction of health-related quality of life in Parkinson's disease. Most of these challenging problems posed by neuroscience involve new Bayesian network designs that can cope with multiple class variables, small sample sizes, or labels annotated by several experts
Contribuciones al análisis de problemas supercomplejos de toma de decisiones
Los fundamentos de la TeorÃa de la Decisión Bayesiana proporcionan
un marco coherente en el que se pueden resolver los problemas
de toma de decisiones. La creciente disponibilidad de ordenadores potentes
está llevando a tratar problemas cada vez más complejos con
numerosas fuentes de incertidumbre multidimensionales; varios objetivos
conflictivos; preferencias, metas y creencias cambiantes en el
tiempo y distintos grupos afectados por las decisiones. Estos factores,
a su vez, exigen mejores herramientas de representación de problemas;
imponen fuertes restricciones cognitivas sobre los decisores y conllevan
difÃciles problemas computacionales. Esta tesis tratará estos tres
aspectos.
En el CapÃtulo 1, proporcionamos una revisión crÃtica de los principales
métodos gráficos de representación y resolución de problemas,
concluyendo con algunas recomendaciones fundamentales y generalizaciones.
Nuestro segundo comentario nos lleva a estudiar tales métodos
cuando sólo disponemos de información parcial sobre las preferencias
y creencias del decisor. En el CapÃtulo 2, estudiamos este problema
cuando empleamos diagramas de influencia (DI). Damos un algoritmo
para calcular las soluciones no dominadas en un DI y analizamos varios
conceptos de solución ad hoc.
El último aspecto se estudia en los CapÃtulos 3 y 4. Motivado
por una aplicación de gestión de embalses, introducimos un método
heurÃstico para resolver problemas de decisión secuenciales. Como
muestra resultados muy buenos, extendemos la idea a problemas secuenciales
generales y cuantificamos su bondad.
Exploramos después en varias direcciones la aplicación de métodos
de simulación al Análisis de Decisiones. Introducimos primero métodos
de Monte Cario para aproximar el conjunto no dominado en problemas
continuos. Después, proporcionamos un método de Monte Cario
basado en cadenas de Markov para problemas con información completa
con estructura general: las decisiones y las variables aleatorias
pueden ser continuas, y la función de utilidad puede ser arbitraria.
Nuestro esquema es aplicable a muchos problemas modelizados como
DI.
Finalizamos con un capÃtulo de conclusiones y problemas abiertos.---ABSTRACT---The foundations of Bayesian Decisión Theory provide a coherent
framework in which decisión making problems may be solved. With
the advent of powerful computers and given the many challenging
problems we face, we are gradually attempting to solve more and
more complex decisión making problems with high and multidimensional
uncertainty, múltiple objectives, influence of time over decisión
tasks and influence over many groups. These complexity factors demand
better representation tools for decisión making problems; place
strong cognitive demands on the decison maker judgements; and lead
to involved computational problems. This thesis will deal with these
three topics.
In recent years, many representation tools have been developed for
decisión making problems. In Chapter 1, we provide a critical review
of most of them and conclude with recommendations and generalisations.
Given our second query, we could wonder how may we deal with
those representation tools when there is only partial information. In
Chapter 2, we find out how to deal with such a problem when it
is structured as an influence diagram (ID). We give an algorithm to
compute nondominated solutions in ID's and analyse several ad hoc
solution concepts.-
The last issue is studied in Chapters 3 and 4. In a reservoir management
case study, we have introduced a heuristic method for solving
sequential decisión making problems. Since it shows very good performance,
we extend the idea to general problems and quantify its
goodness.
We explore then in several directions the application of simulation
based methods to Decisión Analysis. We first introduce Monte
Cario methods to approximate the nondominated set in continuous
problems. Then, we provide a Monte Cario Markov Chain method
for problems under total information with general structure: decisions
and random variables may be continuous, and the utility function may
be arbitrary. Our scheme is applicable to many problems modeled as
IDs.
We conclude with discussions and several open problems
Maximizing the number of polychronous groups in spiking networks
In this paper we investigate the effect of biasing the axonal connection delay values in the number of polychronous groups produced for a spiking neuron network model. We use an estimation of distribution algorithm (EDA) that learns tree models to search for optimal delay configurations. Our
results indicate that the introduced approach can be used to considerably increase the number of such groups
Interval-based ranking in noisy evolutionary multiobjective optimization
As one of the most competitive approaches to multi-objective optimization, evolutionary algorithms have been shown to obtain very good results for many realworld multi-objective problems. One of the issues that can affect the performance of these algorithms is the uncertainty in the quality of the solutions which is usually represented with the noise in the objective values. Therefore, handling noisy objectives in evolutionary multi-objective optimization algorithms becomes very important and is gaining more attention in recent years. In this paper we present ?-degree Pareto dominance relation for ordering the solutions in multi-objective optimization when the values of the objective functions are given as intervals. Based on this dominance relation, we propose an adaptation of the non-dominated sorting algorithm for ranking the solutions. This ranking method is then used in a standardmulti-objective evolutionary algorithm and a recently proposed novel multi-objective estimation of distribution algorithm based on joint variable-objective probabilistic modeling, and applied to a set of multi-objective problems with different levels of independent noise. The experimental results show that the use of the proposed method for solution ranking allows to approximate Pareto sets which are considerably better than those obtained when using the dominance probability-based ranking method, which is one of the main methods for noise handling in multi-objective optimization
Multi-dimensional classification with super-classes
The multi-dimensional classification problem is a generalisation of the recently-popularised task of multi-label classification, where each data instance is associated with multiple class variables. There has been relatively little research carried out specific to multi-dimensional classification and, although one of the core goals is similar (modelling dependencies among classes), there are important differences; namely a higher number of possible classifications. In this paper we present method for multi-dimensional classification, drawing from the most relevant multi-label research, and combining it with important novel developments. Using a fast method to model the conditional dependence between class variables, we form super-class partitions and use them to build multi-dimensional learners, learning each super-class as an ordinary class, and thus explicitly modelling class dependencies. Additionally, we present a mechanism to deal with the many class values inherent to super-classes, and thus make learning efficient. To investigate the effectiveness of this approach we carry out an empirical evaluation on a range of multi-dimensional datasets, under different evaluation metrics, and in comparison with high-performing existing multi-dimensional approaches from the literature. Analysis of results shows that our approach offers important performance gains over competing methods, while also exhibiting tractable running time
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
Univariate and bivariate truncated von Mises distributions
In this article we study the univariate and bivariate truncated von Mises distribution, as a generalization of the von Mises distribution (\cite{jupp1989}), (\cite{mardia2000directional}). This implies the addition of two or four new truncation parameters in the univariate and, bivariate cases, respectively. The results include the definition, properties of the distribution and maximum likelihood estimators for the univariate and bivariate cases. Additionally, the analysis of the bivariate case shows how the conditional distribution is a truncated von Mises distribution, whereas the marginal distribution that generalizes the distribution introduced in \cite{repe}. From the viewpoint of applications, we test the distribution with simulated data, as well as with data regarding leaf inclination angles (\cite{safari}) and dihedral angles in protein chains (\cite{prote}). This research aims to assert this probability distribution as a potential option for modelling or simulating any kind of phenomena where circular distributions are applicable.\pa
Evolutionary computation of forests with Degree- and Role-Constrained Minimum Spanning Trees
Finding the degree-constrained minimum spanning tree (DCMST) of a graph is a widely studied NP-hard problem. One of its most important applications is network design. Here we deal with a new variant of the DCMST problem, which consists of finding not only the degree- but also the role-constrained minimum spanning tree (DRCMST), i.e., we add constraints to restrict the role of the nodes in the tree to root, intermediate or leaf node. Furthermore, we do not limit the number of root nodes to one, thereby, generally, building a forest of DRCMSTs. The modeling of network design problems can benefit from the possibility of generating more than one tree and determining the role of the nodes in the network. We propose a novel permutation-based representation to encode these forests. In this new representation, one permutation simultaneously encodes all the trees to be built. We simulate a wide variety of DRCMST problems which we optimize using eight different evolutionary computation algorithms encoding individuals of the population using the proposed representation. The algorithms we use are: estimation of distribution algorithm, generational genetic algorithm, steady-state genetic algorithm, covariance matrix adaptation evolution strategy, differential evolution, elitist evolution strategy, non-elitist evolution strategy and particle swarm optimization. The best results are for the estimation of distribution algorithms and both types of genetic algorithms, although the genetic algorithms are significantly faster. -------------------------------------------------------------------------------------------------- Trabajo publicado en: Antón Sánchez, Laura; Bielza Lozoya, Maria Concepcion y Larrañaga Múgica, Pedro (2017). Network Design through Forests with Degree- and Role-constrained Minimum Spanning Trees. "Journal of Heuristics ", v. 23 (n. 1); pp. 31-51. ------------------------------------------
Predicting EQ-5D from the Parkinson's disease questionnaire PDQ-8 using multi-dimensional Bayesian network classifiers
The impact of the Parkinson's disease and its treatment on the patients' health-related quality of life can be estimated either by means of generic measures such as the european quality of Life-5 Dimensions (EQ-5D) or specific measures such as the 8-item Parkinson's disease questionnaire (PDQ-8). In clinical studies, PDQ-8 could be used in detriment of EQ-5D due to the lack of resources, time or clinical interest in generic measures. Nevertheless, PDQ-8 cannot be applied in cost-effectiveness analyses which require generic measures and quantitative utility scores, such as EQ-5D. To deal with this problem, a commonly used solution is the prediction of EQ-5D from PDQ-8. In this paper, we propose a new probabilistic method to predict EQ-5D from PDQ-8 using multi-dimensional Bayesian network classifiers. Our approach is evaluated using five-fold cross-validation experiments carried out on a Parkinson's data set containing 488 patients, and is compared with two additional Bayesian network-based approaches, two commonly used mapping methods namely, ordinary least squares and censored least absolute deviations, and a deterministic model. Experimental results are promising in terms of predictive performance as well as the identification of dependence relationships among EQ-5D and PDQ-8 items that the mapping approaches are unable to detec