As a probability-based statistical classification method, the Naïve Bayesian classifier has gained wide popularity despite its assumption that attributes are conditionally mutually independent given the class label. Improving the predictive accuracy and achieving dimensionality reduction for statistical classifiers has been an active research area in datamining. Our experimental results suggest...

In this paper, we empirically evaluate algorithms for learning four Bayesian network (BN) classifiers: Naïve-Bayes, tree augmented Naïve-Bayes (TANs), BN augmented NaïveBayes (BANs) and general BNs (GBNs), where the GBNs and BANs are learned using two variants of a conditional independence based BN-learning algorithm. Experimental results show the GBNs and BANs learned using the proposing learn...

The paper presents fraud detection method to predict and analyze fraud patterns from data. To generate classifiers, we apply the Naïve Bayesian Classification, and Decision Tree-Based algorithms. A brief description of the algorithm is provided along with its application in detecting fraud. The same data is used for both the techniques. We analyze and interpret the classifier predictions. The m...

Text categorization is a fundamental methodology of text mining and a hot topic of the research of data mining and web mining in recent years. It plays an important role in building traditional information retrieval, web indexing architecture, Web information retrieval, and so on. This paper presents an improved algorithm of text categorization that combines the feature weighting technique with...

Naïve Bayesian has been widely used in spam filter because it simply and it also could classify texts more correctly and quickly. However, in the process of classifying and filtering, the traditional method doesn't consider the different features between the spam mail and the legitimate mail, and it also doesn't take into account the loss of misclassifying legitimate mail as spam, so there are ...

With the increase in information on the Internet, the strive to find more effective tools for distinguishing between interesting and non-interesting material is increasing. Drawing analogies from the biological immune system, this paper presents an immune-inspired algorithm called AISEC that is capable of continuously classifying electronic mail as interesting and non-interesting without the ne...

This paper represents a new context-aware learning system to provide services in ubiquitous computing environment. The aim is to precisely decide which services each user provides. To achieve this goal, we design a preprocessing method (i.e., context modeling) to obtain good information which represents user’s characteristics from context-aware information (i.e., user profiles) which consists o...