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P Larrañaga

Publications and source records attributed to P Larrañaga.

7 recordsLinked to original sources
PubMed2005

Globally multimodal problem optimization via an estimation of distribution algorithm based on unsupervised learning of Bayesian networks.

Many optimization problems are what can be called globally multimodal, i.e., they present several global optima. Unfortunately, this is a major source of difficulties for most estimation of distribution algorithms, making their effectiveness and efficiency degrade, due to genetic drift. With the aim of overcoming these drawbacks for discrete globally multimodal problem optimization, this paper introduces and evaluates a new estimation of distribution algorithm based on unsupervised learning of Bayesian networks. We report the satisfactory results of our experiments with symmetrical binary optimization problems.

Algorithms↗
PubMed2001

Using Bayesian networks in the construction of a bi-level multi-classifier. A case study using intensive care unit patients data.

Combining the predictions of a set of classifiers has shown to be an effective way to create composite classifiers that are more accurate than any of the component classifiers. There are many methods for combining the predictions given by component classifiers. We introduce a new method that combine a number of component classifiers using a Bayesian network as a classifier system given the component classifiers predictions. Component classifiers are standard machine learning classification algorithms, and the Bayesian network structure is learned using a genetic algorithm that searches for the structure that maximises the classification accuracy given the predictions of the component classifiers. Experimental results have been obtained on a datafile of cases containing information about ICU patients at Canary Islands University Hospital. The accuracy obtained using the presented new approach statistically improve those obtained using standard machine learning methods.

Algorithms↗
PubMed2001

Feature subset selection by genetic algorithms and estimation of distribution algorithms. A case study in the survival of cirrhotic patients treated with TIPS.

The transjugular intrahepatic portosystemic shunt (TIPS) is an interventional treatment for cirrhotic patients with portal hypertension. In the light of our medical staff's experience, the consequences of TIPS are not homogeneous for all the patients and a subgroup dies in the first 6 months after TIPS placement. Actually, there is no risk indicator to identify this subgroup of patients before treatment. An investigation for predicting the survival of cirrhotic patients treated with TIPS is carried out using a clinical database with 107 cases and 77 attributes. Four supervised machine learning classifiers are applied to discriminate between both subgroups of patients. The application of several feature subset selection (FSS) techniques has significantly improved the predictive accuracy of these classifiers and considerably reduced the amount of attributes in the classification models. Among FSS techniques, FSS-TREE, a new randomized algorithm inspired on the new EDA (estimation of distribution algorithm) paradigm has obtained the best average accuracy results for each classifier.

Algorithms↗
PubMed1992

[Prognostic factors in HIV-infected heroin addicts: a multivariate analysis of nonspecific serological factors in the evolution of the infection].

We present a prognostic analysis of the quantifying of serum levels of beta 2 microglobulin, neopterina, IL-2 soluble receptor and three major classes of immunoglobulins, in a group of 68 heroin-addicts infected by the human immune deficiency virus, type I, clinically assessed for a period of at least three years. High levels of any of these unspecific serologic factors were correlated with the illness progression. Survival curves were generated with the categorized variables, showed a significant decrease on the time interval prior to the diagnosis of AIDS, in the patients with these variables assigned on the higher groups, being neopterine and IgA the more predictive factors when the Cox proportional regression model is applied. We conclude that the quantifying of these unspecific serum factors provides a useful information regarding the clinical evolution of heroin-addicts with HIV infection.

Acquired Immunodeficiency Syndrome↗
PubMed1988

Prognostic score in acute meningococcemia.

A prognostic score for children with acute meningococcemia is proposed. We reviewed 176 consecutive patients with acute meningococcemia with ten fatalities admitted to our pediatric ICU in the last 3 yr. The score was obtained from patients in shock, using a stepwise linear discriminant analysis of 18 clinical and laboratory variables on admission. Nine variables showed a significant discriminant power in predicting survival and death: coma, base excess, platelets, glucose, temperature, WBC, sex, purpura, and CSF. The score predicted survival in 100% and death in 91%. The predictive values were significantly better than evaluation by the frequencies of the usual clinical and laboratory variables.

Blood Cells↗
PubMedDate not supplied

Predicting survival in malignant skin melanoma using Bayesian networks automatically induced by genetic algorithms. An empirical comparison between different approaches.

In this work we introduce a methodology based on genetic algorithms for the automatic induction of Bayesian networks from a file containing cases and variables related to the problem. The structure is learned by applying three different methods: The Cooper and Herskovits metric for a general Bayesian network, the Markov blanket approach and the relaxed Markov blanket method. The methodologies are applied to the problem of predicting survival of people after 1, 3 and 5 years of being diagnosed as having malignant skin melanoma. The accuracy of the obtained models, measured in terms of the percentage of well-classified subjects, is compared to that obtained by the so-called Naive-Bayes. In the four approaches, the estimation of the model accuracy is obtained from the 10-fold cross-validation method.

Algorithms↗