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, ...

A naive Bayes classifier is a simple probabilistic classifier based on applying Bayes’ theorem with naive independence assumption. The explanatory variables (Xi) are assumed to be independent from the target variable (Y ). Despite this strong assumption this classifier has proved to be very effective on many real applications and is often used on data stream for supervised classification. The n...

Many algorithms have been proposed for the machine learning task of classification. One of the simplest methods, the naive Bayes classifier, has often been found to give good performance despite the fact that its underlying assumptions (of independence and a normal distribution of the variables) are perhaps violated. In previous work, we applied naive Bayes and other standard algorithms to a br...

A lot of models used to represent preferences or other relations have been characterized in the framework of measurement theory. To assess the empirical validity of one of these models, we can either test the model itself or test the axioms characterizing it. When a model is rejected, the latter approach has the advantage of indicating why it is rejected. The available statistical techniques fo...

Naive Bayes is a well-known and studied algorithm both in statistics and machine learning. Bayesian learning algorithms represent each concept with a single probabilistic summary. In this paper we present an iterative approach to naive Bayes. The Iterative Bayes begins with the distribution tables built by the naive Bayes. Those tables are iteratively updated in order to improve the probability...

Heart failure (ARF) is a health problem that has relatively high mortality and morbidity rates in developed or developing countries, including Indonesia. In 2016, WHO stated 17.5 million people died from cardiovascular disease, while 2008, HF disease represented 31% of patient deaths worldwide. One the new breakthroughs for early diagnosis utilizing data mining techniques. this study, Correlate...

The definition of what makes an article interesting varies from user to user and continually evolves even for a single user. As a result, for news recommendation systems, useless document features can not be determined a priori and all features are usually considered for interestingness classification. Consequently, the presence of currently useless features degrades classification performance ...

Naive Bayes is a simple Bayesian network classifier with strong independence assumptions among features. This classifier despite its strong independence assumptions, often performs well in practice. It is believed that relaxing the independence assumptions of naive Bayes may improve the performance of the resulting structure. Augmented Bayesian Classifiers relax the independence assumptions of ...

In this paper, we investigate the use of multivariate Poisson model and feature weighting to learn naive Bayes text classifier. Our new naive Bayes text classification model assumes that a document is generated by a multivariate Poisson model while the previous works consider a document as a vector of binary term features based on the presence or absence of each term. We also explore the use of...

The naive Bayes classifier has proved to be very effective on many real data applications. Its performance usually benefits from an accurate estimation of univariate conditional probabilities and from variable selection. However, although variable selection is a desirable feature, it is prone to overfitting. In this paper, we introduce a Bayesian regularization technique to select the most prob...