Basilio Sierra

Gene selection for cancer classification using wrapper approaches

Despite the fact that cancer classification has considerably improved, nowadays a general method that classifies known types of cancer has not yet been developed. In this work, we propose the use of supervised classification techniques, coupled with feature subset selection algorithms, to automatically perform this classification in gene expression datasets. Due to the large number of features of gene expression datasets, the search of a highly accurate combination of features is done by means of the new Estimation of Distribution Algorithms paradigm. In order to assess the accuracy level of the proposed approach, the naive-Bayes classification algorithm is employed in a wrapper form. Promising results are achieved, in addition to a considerable reduction in the number of genes. Stating the optimal selection of genes as a search task, an automatic and robust choice in the genes finally selected is performed, in contrast to previous works that research the same types of problems.

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.

Feature subset selection by genetic algorithms and estimation of distribution algorithms

The transjugular intrahepatic portosystemic shunt (TIPS) is an interventional treatment for cirrhotic patients with portal hypertension. In the light of our medical staffs 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.

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.

Prototype Selection and Feature Subset Selection by 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 staffs experience, the consequences of TIPS are not homogeneous for all the patients and a subgroup dies in the first six months after TIPS placement. An investigation for predicting the conduct of cirrhotic patients treated with TIPS is carried out using a clinical database with 107 cases and 77 attributes. We have applied a new Estimation of Distribution Algorithms based approach in order to perform a Prototype and Feature Subset Selection to improve the classification accuracy obtained using all the variables and all the cases. Used paradigms are K-Nearest Neighbours, Artificial Neural Networks and Classification Trees.

On the use of Bayesian Networks to develop behaviours for mobile robots

Bayesian Networks are models which capture uncertainties in terms of probabilities that can be used to perform reasoning under uncertainty. This paper presents an attempt to use Bayesian Networks as a learning technique to manage task execution in mobile robotics. To learn the Bayesian Network structure from data, the K2 structural learning algorithm is used, combined with three different net evaluation metrics. The experiment led to a new hybrid multiclassifying system resulting from the combination of 1-NN with the Bayesian Network, that allows one to use the power of the Bayesian Network while avoiding the computational burden of the reasoning mechanism - the so-called evidence propagation process. As an application example we present an approach of the presented paradigm to implement a door-crossing behaviour in a mobile robot using only sonar readings, in an environment with smooth walls and doors. Both the performance of the learning mechanism and the experiments run in the real robot-environment system show that Bayesian Networks are valuable learning mechanisms, able to deal with the uncertainty and variability inherent to such systems.

Learning Bayesisan Networks by Genetic Algorithms: A Case Study in the Prediction of Survival in Malignant Skin Melanoma

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 methodology is applied to the problem of predicting survival of people after one, three and five years of being diagnosed as having malignant skin melanoma. The accuracy of the obtained model, measured in terms of the percentage of well-classified subjects, is compared to that obtained by the so-called Naive-Bayes. In both cases, the estimation of the model accuracy is obtained from the 10-fold cross-validation method.

Layered architecture for real time sign recognition: Hand gesture and movement

Sign and gesture recognition offers a natural way for human-computer interaction. This paper presents a real time sign recognition architecture including both gesture and movement recognition. Among the different technologies available for sign recognition data gloves and accelerometers were chosen for the purposes of this research. Due to the real time nature of the problem, the proposed approach works in two different tiers, the segmentation tier and the classification tier. In the first stage the glove and accelerometer signals are processed for segmentation purposes, separating the different signs performed by the system user. In the second stage the values received from the segmentation tier are classified. In an effort to emphasize the real use of the architecture, this approach deals specially with problems like sensor noise and simplification of the training phase.

Application of Bayesian networks in prognostics for a new Integrated Vehicle Health Management concept

The aeronautics industry is attempting to implement important changes to its maintenance strategy. The article presents a new framework for making final decision on aeroplane maintenance actions. It emphasizes on the use of prognostics within this global framework to replace corrective and Preventive Maintenance practise for a predictive maintenance to minimize the cost of the maintenance support and to increase aircraft/fleet operability. The main objective of the article is to show the Bayesian network model as a useful technique for prognosis. The specific use case for predicting brake wear on the plane is developed based on this technique. The network allows estimate brake wear from the aircraft operational plan. This model, together with other models to make predictions for various components of the aeroplane (that should be monitored) offers a forward-looking approach of the status of the plane, allowing later the evaluation of different operational plans based on operational risk assessment and economic cost of each one of them depending on the scheduled checks.

LEARNING BAYESIAN NETWORKS IN THE SPACE OF ORDERINGS WITH ESTIMATION OF DISTRIBUTION ALGORITHMS

The search for the optimal ordering of a set of variables in order to solve a computational problem is a dicult y that can appear in several circumstances. One of these situations is the automatic learning of a network structure, for example, a Bayesian Network structure (BN) starting from a dataset. Searching in the space of structures is often unmanageable, especially if the number of variables is high. Popular heuristic approaches, like Cooper and Herskovits’s K2 algorithm, depend on a given ordering of variables. Estimation of Distribution Algorithms (EDAs) are a new paradigm for Evolutionary Computation that have been used as a search engine in the BN structure learning problem. In this paper, we will use two dieren t EDAs to obtain not the best structure, but the optimal ordering of variables for the K2 algorithm: UMDA and MIMIC, both of them in discrete and continuous domains. We will also check whether the individual representation and its relation to the corresponding ordering play important roles, and whether MIMIC outperforms the results of UMDA.