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In this paper we propose a new methodology for link-based document classification based on probabilistic classifiers and Bayesian networks. We also report the results obtained of its application to the XML Document Mining Track of INEX΄09. [ABSTRACT FROM AUTHOR]

In this article, we shall present a method for combining classification trees obtained by a simple method from the imprecise Dirichlet model (IDM) and uncertainty measures on closed and convex sets of probability distributions, otherwise known as credal sets. Our combine method has principally two characteristics: it obtains a high percentage of correct classifications using a few number of classification trees and it can be parallelized to apply on very large databases. [ABSTRACT FROM AUTHOR]

In this work, we present a semi-naive Bayes classifier that searches for dependent attributes using different filter approaches. In order to avoid that the number of cases of the compound attributes be too high, a grouping procedure is applied each time after two variables are merged. This method tries to group two or more cases of the new variable into an unique value. In an emperical study, we show as this approach outperforms the naive Bayes classifier in a very robust way and reaches the performance of the Pazzani's semi-naive Bayes [1] without the high cost of a wrapper search. [ABSTRACT FROM AUTHOR]

In the field of attribute mining, several feature selection methods have recently appeared indicating that the use of sets of decision trees learnt from a data set can be an useful tool for selecting relevant and informative variables regarding to a main class variable. With this aim, in this study, we claim that the use of a new split criterion to build decision trees outperforms another classic split criterions for variable selection purposes. We present an experimental study on a wide and different set of databases using only one decision tree with each split criterion to select variables for the Naive Bayes classifier. [ABSTRACT FROM AUTHOR]

In this work, we present some significant improvements for for feature selection in wrapper methods. They are two: the first of them consists in a proper preordering of the feature set; and the second one consists in the application of an irrelevant feature elimination method, where the irrelevance condition is subjected to the partial selected feature subset by the wrapper method. We validate these approaches with the Diffuse Large B-Cell Lymphoma subtype classification problem and we show that these two changes are an important improvement in the computation cost and the classification accuracy of these wrapper methods in this domain. [ABSTRACT FROM AUTHOR]