In this paper, we empirically analyze the behaviors of the Variational Bayes algorithm for the mixture model. While the Variational Bayesian learning has provided computational tractability and good generalization performance in many applications, little has been done to investigate its properties. Recently, the stochastic complexity of mixture models in the Variational Bayesian learning was cl...

Efficient probabilistic inference is key to the success of statistical relational learning. One issue that increases the cost of inference is the presence of irrelevant random variables. The Bayes-ball algorithm can identify the requisite variables in a propositional Bayesian network and thus ignore irrelevant variables. This paper presents a lifted version of Bayes-ball, which works directly o...

The Naïve Bayes document classifier has been used in many document classification algorithms [1], but is only really useful on a small subset of documents due to it’s many shortcomings [2]. By augmenting the basic functionality of the simple Naïve Bayes classifier, the classification algorithm can be applied to a much wider range of documents. This paper investigates the advantages which can be...

We present the system nFOIL. It tightly integrates the naı̈ve Bayes learning scheme with the inductive logic programming rule-learner FOIL. In contrast to previous combinations, which have employed naı̈ve Bayes only for post-processing the rule sets, nFOIL employs the naı̈ve Bayes criterion to directly guide its search. Experimental evidence shows that nFOIL performs better than both its base line...

Empirical Bayes methods are privileged in data mining because they can absorb prior information on model parameters and are free of choosing tuning parameters. We proposed an iterated conditional modes/medians (ICM/M) algorithm to implement empirical Bayes selection of massive variables while incorporating sparsity or more complicated a priori information. The algorithm is constructed on the ba...

Due to today’s advancement in technology and businesses, fraud detection has become a critical component of financial transactions. Considering vast amounts of data in large datasets, it becomes more difficult to detect fraud transactions manually. In this research, we propose a combined method using both data mining and statistical tasks, utilizing feature selection, resampling and cost-...