In real-world data mining applications, an accurate ranking is same important to a accurate classification. Naive Bayes (simply NB) has been widely used in data mining as a simple and effective classification and ranking algorithm. Since its conditional independence assumption is rarely true, numerous algorithms have been proposed to improve Naive Bayes, for example, SBC[1] and TAN[2]. Indeed, ...

Designed for multi-relational explore and learn about important device data classification, and can be widely used in many fields. New classification algorithm Union, naive Bayes, which is the main function of what is known in the literature for the application of multiple classification Union relational environment. The results showed that naive Bayes achieves greater accuracy compared to exis...

This paper presents a new approach to the unsupervised training of Bayesian network classifiers. Three models have been analysed: the Chow and Liu (CL) multinets; the treeaugmented naive Bayes; and a new model called the simple Bayesian network classifier, which is more robust in its structure learning. To perform the unsupervised training of these models, the classification maximum likelihood ...

In this paper, we attempt to automatically annotate the Penn Chinese Treebank with semantic dependency structure. Initially a small portion of the Penn Chinese Treebank was manually annotated with headword and semantic dependency relations. An initial investigation is then done using a Naive Bayesian Classifier and some handcrafted rules. The results show that the algorithms and proposed approa...

Naive Bayes is one of the most efficient and effective inductive learning algorithms for machine learning and data mining. Its competitive performance in classification is surprising, because the conditional independence assumption on which it is based, is rarely true in realworld applications. An open question is: what is the true reason for the surprisingly good performance of naive Bayes in ...

Patients with liver disease have been continuously increasing because of excessive consumption of alcohol, inhalation of harmful gases, intake of contaminated food, pickles and drugs. Automatic classification tools may reduce burden on doctors. This paper evaluates the selected classification algorithms for the classification of some liver patient datasets. Classification algorithms considered ...

In 1981 Rubin introduced the Bayesian bootstrap and argued that it was the natural Bayesian analogue to the usual bootstrap. We show here that when estimating a population quantile in a nonparametric problem it yields estimators that are often preferred to the natural naive estimators based on the order statistic. AMS 1980 Subject Classification: 62G05, 62C15 and 62G30