Multiple Classifier Systems (MCSs) allow evaluation of the uncertainty of classification outcomes that is of crucial importance for safety critical applications. The uncertainty of classification is determined by a trade-off between the amount of data available for training, the classifier diversity and the required performance. The interpretability of MCSs can also give useful information for ...

One of the most relevant task in Sentiment Analysis is Polarity Classification. In this paper, we discuss how to explore the potential of ensembles of classifiers and propose a voting mechanism based on Bayesian Model Averaging (BMA). An important issue to be addressed when using ensemble classification is the model selection strategy. In order to help in selecting the best ensemble composition...

Naïve Bayes classifiers are simple probabilistic classifiers. Classification extracts patterns by using data file with a set of labeled training examples and is currently one of the most significant areas in data mining. However, Naïve Bayes assumes the independence among the features. Structural learning among the features thus helps in the classification problem. In this study, the use of str...

Various machine learning techniques have been proposed for the development of prognostic models, including those based on Bayesian networks. An advantage of a Bayesian network compared to many other classifiers is that the model can provide insight by representing the temporal structure of the domain. While it has been shown that Bayesian networks can perform well in terms of classification acc...

Approximate Bayesian Gaussian process (GP) classification techniques are powerful nonparametric learning methods, similar in appearance and performance to Support Vector machines. Based on simple probabilistic models, they render interpretable results and can be embedded in Bayesian frameworks for model selection, feature selection, etc. In this paper, by applying the PAC-Bayesian theorem of nc...

Using PCA based Bayesian Classification to monitor the real plant with different operated conditions is proposed. Since the process condition s are time-variant, as the PCA subspace cannot explain the data of new events, the PCA should be reperformed. In this work the method of updating Bayesian model is developed. Only the data of new events are trained in the newer subspace. The ability of PC...

Parsimonious Markov models have been recently developed as a generalization of variable order Markov models. Many practical applications involve a setting with latent variables, with a common example being mixture models. Here, we propose a Bayesian model averaging approach for learning mixtures of parsimonious Markov models that is based on Gibbs sampling. The challenging problem is sampling o...