Data mining applications require learning algorithms to have high predictive accuracy, scale up to large datasets, and produce compre-hensible outcomes. Naive Bayes classiier has received extensive attention due to its eeciency, reasonable predictive accuracy, and simplicity. However , the assumption of attribute dependency given class of Naive Bayes is often violated, producing incorrect proba...

We describe an in-depth analysis of spam-filtering performance of a simple Naive Bayes learner and two current variants. A set of seven mailboxes comprising about 65,000 mails from seven different users, as well as a representative snapshot of 25,000 mails which were received over 18 weeks by a single user, were used for evaluation. Our main motivation was to test whether two variants of Naive ...

The relative performance of diierent methods for classiier learning varies across domains. Some recent Instance Based Learning (IBL) methods, such as IB1-MVDM* 10 , use similarity measures based on conditional class probabilities. These probabilities are a key component of Naive Bayes methods. Given this commonality of approach, it is of interest to consider how the diierences between the two m...

The conditional independence assumption of naive Bayes essentially ignores attribute dependencies and is often violated. On the other hand, although a Bayesian network can represent arbitrary attribute dependencies, learning an optimal Bayesian network from data is intractable. The main reason is that learning the optimal structure of a Bayesian network is extremely time consuming. Thus, a Baye...

Bayesian network models are widely used for supervised prediction tasks such as classification. The Naive Bayes (NB) classifier in particular has been successfully applied in many fields. Usually its parameters are determined using ‘unsupervised’ methods such as likelihood maximization. This can lead to seriously biased prediction, since the independence assumptions made by the NB model rarely ...

In this paper, the naive credal classifier, which is a set-valued counterpart of naive Bayes, is extended to a general and flexible treatment of incomplete data, yielding a new classifier called naive credal classifier 2 (NCC2). The new classifier delivers classifications that are reliable even in the presence of small sample sizes and missing values. Extensive empirical evaluations show that, ...

Naive Bayes classiiers can be seen as special probabilistic networks with a star-like structure. They can easily be induced from a dataset of sample cases. However, as most probabilistic approaches, they run into problems, if imprecise (i.e, set-valued) information in the data to learn from has to be taken into account. An approach to handle uncertain as well imprecise information, which recent...

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 ...

Despite its simplicity, the naive Bayes learning scheme performs well on most classiication tasks, and is often signiicantly more accurate than more sophisticated methods. Although the probability estimates that it produces can be inaccurate, it often assigns maximum probability to the correct class. This suggests that its good performance might be restricted to situations where the output is c...

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. This paper presents a variant of the Naive Bayes method, in which the original training set is augmented in the following fashion: Leave-One-Out procedure is applied over the training set, and incorrectly classified i...