We introduce three discriminative parameter learning algorithms for Bayesian network classifiers based on optimizing either the conditional likelihood (CL) or a lower-bound surrogate of the CL. One training procedure is based on the extended Baum-Welch (EBW) algorithm. Similarly, the remaining two approaches iteratively optimize the parameters (initialized to ML) with a 2-step algorithm. In the...

We introduce a Bayesian network classifier less restrictive than Naive Bayes (NB) and Tree Augmented Naive Bayes (TAN) classifiers. Considering that learning an unrestricted network is unfeasible the proposed classifier is confined to be consistent with the breadth-first search order of an optimal TAN. We propose an efficient algorithm to learn such classifiers for any score that decompose over...

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...

Naive-Bayes induction algorithms were previously shown to be surprisingly accurate on many classification tasks even when the conditional independence assumption on which they are based is violated. However, most studies were done on small databases. We show that in some larger databases, the accuracy of Naive-Bayes does not scale up as well as decision trees. We then propose a new algorithm, N...

Selecting a single model for clustering ignores the uncertainty left by finite data as to which is the correct model to describe the dataset. In fact, the fewer samples the dataset has, the higher the uncertainty is in model selection. In these cases, a Bayesian approach may be beneficial, but unfortunately this approach is usually computationally intractable and only approximations are feasibl...

In this paper we present a novel induction algorithm for Bayesian networks. This selective Bayesian network classiier selects a subset of attributes that maximizes predictive accuracy prior to the network learning phase, thereby learning Bayesian networks with a bias for small, high-predictive-accuracy networks. We compare the performance of this classiier with selective and non-selective naive...

Human longevity is a complex phenotype that has a significant genetic predisposition. Like other biological processes, ageing process is governed through the regulation of signaling pathways and transcription factors. The DNA damage theory of ageing suggests that ageing is a consequence of un-repaired DNA damage accumulation. Intensive research has been carried out to elucidate the role of DNA ...

BACKGROUND In the last decade the standard Naive Bayes (SNB) algorithm has been widely employed in multi-class classification problems in cheminformatics. This popularity is mainly due to the fact that the algorithm is simple to implement and in many cases yields respectable classification results. Using clever heuristic arguments "anchored" by insightful cheminformatics knowledge, Xia et al. h...

Classification is an open, old and basic problem in many domains. Recently, a lot of new methods come forth, such as Bayesian Networks. Bayesian Networks, one of probabilistic networks, are a powerful data mining technique for handling uncertainty in complex domains. In this paper, we apply Bayesian Networks Augmented Naive Bayes (BAN) to texture classification of aerial image and propose a new...