In this paper, we present a method for exploratory data analysis of streaming data based on probabilistic graphical models (latent variable models). This method is illustrated by concept drift tracking, using financial client data from a European regional bank. For this particular setting, the anal yzed data spans the period from April 2007 to March 2014 and therefore starts before the beginning of the financial crisis of 2008. The implied changes in the economic climate during this period manifests itself as concept drift in the underlying data generating distribution. We explore and analyze this financial client data using a probabilistic graphical modeling framework that provides an explicit representation of concept drift as an integral part of the model. We show how learning these types of models from data provides additional insight into the hidden mechanisms governing the drift in the domain. We present an iterative approach for identifying disparate factors that jointly account for the drift in the domain. This includes a semantic characterization of one of the main influencing drift factors. Based on the experiences and results obtained from analyzing the financial data, we discuss the applicability of the framework within a more general context.

More and more applications have come with large data sets in the past decade. However, existing algorithms cannot guarantee to scale well on large data. Averaged n-Dependence Estimators (AnDE) allows for flexible learning from out-of-core data, by varying the value of n (number of super parents). Hence, AnDE is especially appropriate for large data learning. In this paper, we propose a sample-based attribute selection technique for AnDE. It needs one more pass through the training data, in which a multitude of approximate AnDE models are built and efficiently assessed by leave-one-out cross validation. The use of a sample reduces the training time. Experiments on 15 large data sets demonstrate that the proposed technique significantly reduces AnDE's error at the cost of a modest increase in training time. This efficient and scalable out-of-core approach delivers superior or comparable performance to typical in-core Bayesian network classifiers.

In this paper we propose a method for scaling up filter-based feature selection in classification problems. We use the conditional mutual information as filter measure and show how the required statistics can be computed in parallel avoiding unnecessary calculations. The distribution of the calculations between the available computing units is determined based on balanced incomplete block designs, a strategy first developed within the area of statistical design of experiments. We show the scalability of our method through a series of experiments on synthetic and real-world datasets.

Ever increasing data quantity makes ever more urgent the need for highly scalable learners that have good classification performance. Therefore, an out-of-core learner with excellent time and space complexity, along with high expressivity (that is, capacity to learn very complex multivariate probability distributions) is extremely desirable. This paper presents such a learner. We propose an extension to the $k$-dependence Bayesian classifier (KDB) that discriminatively selects a sub- model of a full KDB classifier. It requires only one additional pass through the training data, making it a three-pass learner. Our extensive experimental evaluation on $16$ large data sets reveals that this out-of-core algorithm achieves competitive classification performance, and substantially better training and classification time than state-of-the-art in-core learners such as random forest and linear and non-linear logistic regression.

The Averaged One-Dependence Estimators (AODE) classifier is one of the most attractive semi-naive Bayesian classifiers and hence a good alternative to Naive Bayes (NB), as it obtains fairly low error rates maintaining under control the computational complexity. Unfortunately, as most of the methods designed within the framework of Bayesian networks, AODE is exclusively defined to deal with discrete variables. Several approaches to avoid the use of discretization pre-processing techniques have already been presented, all of them involving in lower or greater degree the assumption of (conditional) Gaussian distributions. In this paper, we propose the use of Mixture of Truncated Exponentials (MTEs), whose expressive power to accurately approximate the most commonly used distributions for hybrid networks has already been demonstrated. We perform experiments on the use of MTEs over a large group of datasets for the first time, and we analyze the importance of selecting a proper number of points when learning MTEs for NB and AODE, as we believe, it is decisive to provide accurate results.

AODE (Aggregating One-Dependence Estimators) is considered one of the most interesting representatives of the Bayesian classifiers, taking into account not only the low error rate it provides but also its efficiency. Until now, all the attributes in a dataset have had to be nominal to build an AODE classifier or they have had to be previously discretized. In this paper, we propose two different approaches in order to deal directly with numeric attributes. One of them uses conditional Gaussian networks to model a dataset exclusively with numeric attributes; and the other one keeps the superparent on each model discrete and uses univariate Gaussians to estimate the probabilities for the numeric attributes and multinomial distributions for the categorical ones, it also being able to model hybrid datasets. Both of them obtain competitive results compared to AODE, the latter in particular being a very attractive alternative to AODE in numeric datasets.