Learning Extended Tree Augmented Naive Structures

This work proposes an extended version of the well-known tree-augmented naive Bayes (TAN) classifier where the structure learning step is performed without requiring features to be connected to the class. Based on a modification of Edmonds ’ algorithm, our structure learning procedure explores a superset of the structures that are considered by TAN, yet achieves global optimality of the learning score function in a very efficient way (quadratic in the number of features, the same complexity as learning TANs). We enhance our procedure with a new score function that only takes into account arcs that are relevant to predict the class, as well as an optimization over the equivalent sample size during learning. These ideas may be useful for structure learning of Bayesian networks in general. A range of experiments show that we obtain models with better prediction accuracy than Naive Bayes and TAN, and comparable to the accuracy of the state-of-the-art classifier averaged one-dependence estimator (AODE). We release our implementation of ETAN so that it can be easily installed and run within Weka

Medical image modality classification using discrete Bayesian Networks

In this paper we propose a complete pipeline for medical image modality classification focused on the application of discrete Bayesian network classifiers. Modality refers to the categorization of biomedical images from the literature according to a previously defined set of image types, such as X-ray, graph or gene sequence. We describe an extensive pipeline starting with feature extraction from images, data combination, pre-processing and a range of different classification techniques and models. We study the expressive power of several image descriptors along with supervised discretization and feature selection to show the performance of discrete Bayesian networks compared to the usual deterministic classifiers used in image classification. We perform an exhaustive experimentation by using the ImageCLEFmed 2013 collection. This problem presents a high number of classes so we propose several hierarchical approaches. In a first set of experiments we evaluate a wide range of parameters for our pipeline along with several classification models. Finally, we perform a comparison by setting up the competition environment between our selected approaches and the best ones of the original competition. Results show that the Bayesian Network classifiers obtain very competitive results. Furthermore, the proposed approach is stable and it can be applied to other problems that present inherent hierarchical structures of classes

Positive and unlabeled learning in categorical data

International audienceIn common binary classification scenarios, the presence of both positive and negative examples in training datais needed to build an efficient classifier. Unfortunately, in many domains, this requirement is not satisfied andonly one class of examples is available. To cope with this setting, classification algorithms have been introducedthat learn from Positive and Unlabeled (PU) data. Originally, these approaches were exploited in the context ofdocument classification. Only few works address the PU problem for categorical datasets. Nevertheless, theavailable algorithms are mainly based on Naive Bayes classifiers. In this work we present a new distance basedPU learning approach for categorical data: Pulce. Our framework takes advantage of the intrinsic relationshipsbetween attribute values and exceeds the independence assumption made by Naive Bayes. Pulce, in fact,leverages on the statistical properties of the data to learn a distance metric employed during the classificationtask. We extensively validate our approach over real world datasets and demonstrate that our strategy obtainsstatistically significant improvements w.r.t. state-of-the-art competitors